Datasets:
task_id stringclasses 358
values | category stringclasses 17
values | difficulty stringclasses 3
values | test_methods stringclasses 2
values | model stringclasses 10
values | model_group stringclasses 2
values | condition stringclasses 2
values | generated_code stringlengths 0 17.7k | tests_passed int64 0 15 | tests_total int64 15 15 | all_tests_passed bool 2
classes | joint_score float64 0 100 | size_score float64 0 1 | control_flow_score float64 0 1 | interface_score float64 0 1 | coupling_score float64 0 1 | metric_decisions dict | scored_metrics dict | code_sha256 stringlengths 64 64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
boolean_rules_hard_002 | boolean_rules | hard | EP only | anthropic/claude-sonnet-5 | large | baseline | def boolean_rules_hard_002(a: bool, b: bool, c: bool, d: bool, mode: str) -> bool:
if mode == "ends_opposite_middle_same":
return (a != d) and (b == c)
if mode == "at_least_two_adjacent_true":
return (a and b) or (b and c) or (c and d)
if mode == "one_true_in_every_pair":
return (a o... | 15 | 15 | true | 100 | 1 | 1 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 1,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 1,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 190.16483617504394,
"main_function_nloc": 8,
"max_cognitive_complexity": 12,
"max_cyclomatic_complexity": 15,
"max_fan_out": 0,
"max_maximum_nesting_depth": 1,
"max_nloc": 8,
"max_parameter_count": 5,
"mean_cognitive_complexity"... | 8ce78a3511226389b7bde824141bf0400c90537fbaf337f85ccef761c7ef94cd |
boolean_rules_hard_004 | boolean_rules | hard | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | def boolean_rules_hard_004(p: bool, q: bool, r: bool, s: bool, mode: str) -> bool:
vals = [p, q, r, s]
if mode == "at_least_one_true_at_ends":
return p or s
if mode == "exactly_three_false_and_middle_true":
return sum(vals) == 1 and (q or r)
if mode == "two_true_two_false_no_adjacent_sam... | 15 | 15 | true | 66.25 | 0 | 0.75 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 0,
"main_function_nloc": 0,
"max_cognitive_complexity": 0,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 0,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 165.05865002596164,
"main_function_nloc": 11,
"max_cognitive_complexity": 8,
"max_cyclomatic_complexity": 9,
"max_fan_out": 0,
"max_maximum_nesting_depth": 1,
"max_nloc": 11,
"max_parameter_count": 5,
"mean_cognitive_complexity"... | 41daeb313005941ab4c267173cb0b9743a653609a15dffdfe2b9304287e4f4e9 |
boolean_rules_hard_005 | boolean_rules | hard | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | def boolean_mode_check(a: bool, b: bool, c: bool, d: bool, mode: str) -> bool:
if mode == "first_true_and_at_least_two_false":
return a and ([b, c, d].count(False) >= 2)
if mode == "palindromic":
return a == d and b == c
if mode == "at_least_three_adjacent_same":
return (a == b == c)... | 15 | 15 | true | 100 | 1 | 1 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 1,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 1,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 157.17331799741265,
"main_function_nloc": 8,
"max_cognitive_complexity": 6,
"max_cyclomatic_complexity": 7,
"max_fan_out": 0,
"max_maximum_nesting_depth": 1,
"max_nloc": 8,
"max_parameter_count": 5,
"mean_cognitive_complexity": ... | 1026a3c9867b807c7b85b3a37b2c3595a445e362c8e983fc9813dd3afcbde9e4 |
boolean_rules_hard_009 | boolean_rules | hard | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | def boolean_rules_hard_009(a: bool, b: bool, c: bool, d: bool, mode: str) -> bool:
count = sum([a, b, c, d])
if mode == "all_adjacent_equal":
return a == b == c == d
elif mode == "exactly_two_true_and_first_false":
return count == 2 and not a
elif mode == "majority_true_and_last_true":
... | 15 | 15 | true | 80 | 0.2 | 1 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 0,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 125.09775004326937,
"main_function_nloc": 9,
"max_cognitive_complexity": 5,
"max_cyclomatic_complexity": 6,
"max_fan_out": 0,
"max_maximum_nesting_depth": 1,
"max_nloc": 9,
"max_parameter_count": 5,
"mean_cognitive_complexity": ... | 2d775b4bd1f0711f639cfdfe15c9432263eccf17a1f3771ce8f4ac2fc43bf5b4 |
boolean_rules_medium_004 | boolean_rules | hard | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | def boolean_rule_switch(a: bool, b: bool, c: bool, d: bool, mode: str) -> bool:
vals = [a, b, c, d]
true_count = sum(vals)
if mode == "all_true":
return true_count == 4
if mode == "any_two_true":
return true_count >= 2
if mode == "exactly_one_false":
return true_count == 3
... | 15 | 15 | true | 71.25 | 0.2 | 0.75 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 0,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 0,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 59.794705707972525,
"main_function_nloc": 10,
"max_cognitive_complexity": 3,
"max_cyclomatic_complexity": 4,
"max_fan_out": 0,
"max_maximum_nesting_depth": 1,
"max_nloc": 10,
"max_parameter_count": 5,
"mean_cognitive_complexity"... | 25afb1bc6badc81c93c24036f41bd60058877871f3fcec73997e7184d8ee212a |
boolean_rules_medium_005 | boolean_rules | medium | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | def boolean_rule_check(p: bool, q: bool, r: bool, s: bool, operator: str) -> bool:
count = sum([p, q, r, s])
if operator == "nand":
return count < 4
if operator == "majority":
return count >= 3
if operator == "xor3":
return count == 3
return False | 15 | 15 | true | 80 | 0.2 | 1 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 0,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 59.794705707972525,
"main_function_nloc": 9,
"max_cognitive_complexity": 3,
"max_cyclomatic_complexity": 4,
"max_fan_out": 0,
"max_maximum_nesting_depth": 1,
"max_nloc": 9,
"max_parameter_count": 5,
"mean_cognitive_complexity": ... | a1c4124e086bcde9c9b994334b38052d482e39541e1ed30804ca859c3416ee6f |
boolean_rules_medium_006 | boolean_rules | hard | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | def boolean_rule_checker(flag1: bool, flag2: bool, flag3: bool, flag4: bool, mode: str) -> bool:
flags = [flag1, flag2, flag3, flag4]
if mode == "alternating":
return all(flags[i] != flags[i + 1] for i in range(3))
if mode == "at_least_three_false":
return flags.count(False) >= 3
if mode... | 15 | 15 | true | 53.75 | 0.2 | 0.25 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 0,
"max_cognitive_complexity": 0,
"max_cyclomatic_complexity": 0,
"max_fan_out": 1,
"max_maximum_nesting_depth": 0,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 0,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 95.90827503317318,
"main_function_nloc": 9,
"max_cognitive_complexity": 7,
"max_cyclomatic_complexity": 6,
"max_fan_out": 0,
"max_maximum_nesting_depth": 2,
"max_nloc": 9,
"max_parameter_count": 5,
"mean_cognitive_complexity": 7... | 10a5289dd247bad1eeed09e62a6003d12e51af1384ddfeb1788ec933fd9944d9 |
boolean_rules_medium_007 | boolean_rules | hard | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | def _adjacent_pair_true(vals: list[bool]) -> bool:
for i in range(len(vals) - 1):
if vals[i] and vals[i + 1]:
return True
return False
def boolean_rules_medium_007(x: bool, y: bool, z: bool, w: bool, mode: str) -> bool:
vals = [x, y, z, w]
true_count = sum(vals)
if mode == "eve... | 15 | 15 | true | 41.25 | 0.2 | 0.75 | 1 | 0 | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 0,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 0,
"max_maximum_nesting_depth": 0,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 1,
"coupling_density": 0.5,
"halstead_volume": 162.84823041805248,
"main_function_nloc": 10,
"max_cognitive_complexity": 4,
"max_cyclomatic_complexity": 5,
"max_fan_out": 1,
"max_maximum_nesting_depth": 2,
"max_nloc": 10,
"max_parameter_count": 5,
"mean_cognitive_complexity":... | 15dffc583cd1c3b12787efb609678625632371fa646f53dc33ba3a7a720ff361 |
boolean_rules_medium_008 | boolean_rules | hard | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | def _exactly_two_false_nonadjacent(vals: list) -> bool:
false_idx = [i for i, v in enumerate(vals) if not v]
if len(false_idx) != 2:
return False
i, j = false_idx
return abs(i - j) != 1
def boolean_rule_check(a: bool, b: bool, c: bool, d: bool, mode: str) -> bool:
vals = [a, b, c, d]
i... | 15 | 15 | true | 41.25 | 0.2 | 0.75 | 1 | 0 | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 0,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 0,
"max_maximum_nesting_depth": 0,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 1,
"coupling_density": 0.5,
"halstead_volume": 112.37013046707143,
"main_function_nloc": 9,
"max_cognitive_complexity": 4,
"max_cyclomatic_complexity": 4,
"max_fan_out": 1,
"max_maximum_nesting_depth": 2,
"max_nloc": 9,
"max_parameter_count": 5,
"mean_cognitive_complexity": 3... | 7bc3199f473473f44ba39c87a5d69a3732807faf3d4259d904ad45d1b6a3a9a7 |
boolean_rules_medium_009 | boolean_rules | hard | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | def boolean_rules_medium_009(a: bool, b: bool, c: bool, d: bool, mode: str) -> bool:
if mode == "exactly_one_true_at_ends":
return (a != d) and not b and not c
if mode == "pairwise_equal":
return (a == b) and (c == d)
if mode == "at_least_one_false_at_middle":
return (not b) or (not ... | 15 | 15 | true | 62.5 | 0.2 | 0.5 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 0,
"max_cognitive_complexity": 0,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 0,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 0,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 155.58941141594505,
"main_function_nloc": 8,
"max_cognitive_complexity": 6,
"max_cyclomatic_complexity": 8,
"max_fan_out": 0,
"max_maximum_nesting_depth": 1,
"max_nloc": 8,
"max_parameter_count": 5,
"mean_cognitive_complexity": ... | 5239f3e398989ba9ae018cdd22ea2abd0e6abf3109ba38a1894ce6899ba3f7a4 |
combinatorial_logic_easy_002 | combinatorial_logic | easy | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | def symmetric_difference_sorted(list1: list[int], list2: list[int]) -> list[int]:
s1 = set(list1)
s2 = set(list2)
result = s1.symmetric_difference(s2)
return sorted(result) | 15 | 15 | true | 80 | 0.2 | 1 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 0,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 0,
"main_function_nloc": 5,
"max_cognitive_complexity": 0,
"max_cyclomatic_complexity": 1,
"max_fan_out": 0,
"max_maximum_nesting_depth": 0,
"max_nloc": 5,
"max_parameter_count": 2,
"mean_cognitive_complexity": 0,
"mean_cyclom... | e88d8ca95defdb369562127898766e78086eabbc90de2bc850595000a9308482 |
combinatorial_logic_easy_003 | combinatorial_logic | easy | EP only | anthropic/claude-sonnet-5 | large | baseline | def diff_sorted_unique(a: list[int], b: list[int]) -> list[int]:
set_a = set(a)
set_b = set(b)
result = set_a - set_b
return sorted(result) | 15 | 15 | true | 75 | 0 | 1 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 0,
"main_function_nloc": 0,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 4.754887502163469,
"main_function_nloc": 5,
"max_cognitive_complexity": 0,
"max_cyclomatic_complexity": 1,
"max_fan_out": 0,
"max_maximum_nesting_depth": 0,
"max_nloc": 5,
"max_parameter_count": 2,
"mean_cognitive_complexity": 0... | 0d1d702f432980efeb9ace1a80ca83f96918ca1c8afa22d9a7500945ff944ac8 |
combinatorial_logic_easy_004 | combinatorial_logic | easy | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | from collections import Counter
def at_least_k_occurrences(nums: list[int], k: int) -> list[int]:
counts = Counter(nums)
result = [num for num, cnt in counts.items() if cnt >= k]
return sorted(result) | 15 | 15 | true | 80 | 0.2 | 1 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 0,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 4.754887502163469,
"main_function_nloc": 4,
"max_cognitive_complexity": 3,
"max_cyclomatic_complexity": 3,
"max_fan_out": 0,
"max_maximum_nesting_depth": 2,
"max_nloc": 4,
"max_parameter_count": 2,
"mean_cognitive_complexity": 3... | 1ebdd7f46a3962af0573719f9f9663b71d396cf59c599a0a7ad922e60a2afa5d |
combinatorial_logic_easy_005 | combinatorial_logic | easy | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | from collections import Counter
def at_least_two_total(a: list[int], b: list[int]) -> list[int]:
counts = Counter(a) + Counter(b)
return sorted(k for k, v in counts.items() if v >= 2) | 15 | 15 | true | 95 | 0.8 | 1 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 0,
"main_function_nloc": 1,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 1,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 15.509775004326936,
"main_function_nloc": 3,
"max_cognitive_complexity": 3,
"max_cyclomatic_complexity": 3,
"max_fan_out": 0,
"max_maximum_nesting_depth": 2,
"max_nloc": 3,
"max_parameter_count": 2,
"mean_cognitive_complexity": ... | 31feec5e913ca7fe17596dc8ba70f0ba99d54f8e7f52611cf62b50c5e6737e6b |
combinatorial_logic_easy_006 | combinatorial_logic | hard | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | def find_missing_in_universe(lists: list[list[int]]) -> list[int]:
flat = [x for sub in lists for x in sub]
if not flat:
return []
universe = set(range(min(flat), max(flat) + 1))
return sorted(universe - set(flat)) | 15 | 15 | true | 75 | 0 | 1 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 0,
"main_function_nloc": 0,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 24,
"main_function_nloc": 6,
"max_cognitive_complexity": 4,
"max_cyclomatic_complexity": 4,
"max_fan_out": 0,
"max_maximum_nesting_depth": 2,
"max_nloc": 6,
"max_parameter_count": 1,
"mean_cognitive_complexity": 4,
"mean_cyclo... | c9368fca98071c8e7b0c4d92d0cecd5c7e185947b707d10bdb66a994b630c0d7 |
combinatorial_logic_easy_007 | combinatorial_logic | hard | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | def shared_from_first_and_others(lists: list[list[int]]) -> list[int]:
if len(lists) < 2 or not lists[0]:
return []
others = set().union(*lists[1:])
return sorted(set(lists[0]) & others) | 15 | 15 | true | 100 | 1 | 1 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 1,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 1,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 38.053747805010275,
"main_function_nloc": 5,
"max_cognitive_complexity": 2,
"max_cyclomatic_complexity": 3,
"max_fan_out": 0,
"max_maximum_nesting_depth": 1,
"max_nloc": 5,
"max_parameter_count": 1,
"mean_cognitive_complexity": ... | dbd747f9df365c5cd704c778306e8468682146429392dcf7b40c6b982d311082 |
combinatorial_logic_easy_008 | combinatorial_logic | easy | EP only | anthropic/claude-sonnet-5 | large | baseline | from collections import Counter
def odd_occurrence_in_lists(lists: list[list[int]]) -> list[int]:
counts = Counter()
for inner in lists:
counts.update(set(inner))
return sorted(num for num, c in counts.items() if c % 2 == 1) | 15 | 15 | true | 63.75 | 0.6 | 0.25 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 0,
"main_function_nloc": 1,
"max_cognitive_complexity": 0,
"max_cyclomatic_complexity": 0,
"max_fan_out": 1,
"max_maximum_nesting_depth": 0,
"max_nloc": 1,
"max_parameter_count": 1,
"mean_cognitive_complexity": 0,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 15.509775004326936,
"main_function_nloc": 5,
"max_cognitive_complexity": 4,
"max_cyclomatic_complexity": 4,
"max_fan_out": 0,
"max_maximum_nesting_depth": 2,
"max_nloc": 5,
"max_parameter_count": 1,
"mean_cognitive_complexity": ... | f270ec57764a6c99128155a099143812b0f6dc61ba8c67358e27c6d7c9a45a54 |
combinatorial_logic_easy_009 | combinatorial_logic | easy | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | from collections import Counter
def common_elements_with_equal_frequency(a: list[int], b: list[int]) -> list[int]:
ca = Counter(a)
cb = Counter(b)
result = [k for k in ca if k in cb and ca[k] == cb[k]]
return sorted(result) | 15 | 15 | true | 57.5 | 0 | 0.5 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 0,
"main_function_nloc": 0,
"max_cognitive_complexity": 0,
"max_cyclomatic_complexity": 0,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 0,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 28.529325012980813,
"main_function_nloc": 5,
"max_cognitive_complexity": 4,
"max_cyclomatic_complexity": 4,
"max_fan_out": 0,
"max_maximum_nesting_depth": 2,
"max_nloc": 5,
"max_parameter_count": 2,
"mean_cognitive_complexity": ... | ccd8ea7b5256bf1150ad6fd2b9bf48a389700687ccb73490cbe9300b4aa84af9 |
combinatorial_logic_hard_001 | combinatorial_logic | medium | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | from collections import Counter
def integers_in_multiple_lists(lists: list[list[int]]) -> list[int]:
if len(lists) < 2:
return []
counts = Counter()
for inner in lists:
counts.update(set(inner))
return sorted(num for num, c in counts.items() if c >= 2) | 15 | 15 | true | 68.75 | 0.8 | 0.25 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 0,
"main_function_nloc": 1,
"max_cognitive_complexity": 0,
"max_cyclomatic_complexity": 0,
"max_fan_out": 1,
"max_maximum_nesting_depth": 0,
"max_nloc": 1,
"max_parameter_count": 1,
"mean_cognitive_complexity": 0,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 13.931568569324174,
"main_function_nloc": 7,
"max_cognitive_complexity": 5,
"max_cyclomatic_complexity": 5,
"max_fan_out": 0,
"max_maximum_nesting_depth": 2,
"max_nloc": 7,
"max_parameter_count": 1,
"mean_cognitive_complexity": ... | 23993f0ec2687ad6fdc44585e59f93ae09ef248845140359eb36db5642bdc7e3 |
combinatorial_logic_hard_002 | combinatorial_logic | medium | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | from collections import Counter
def integers_in_all_but_one(lists: list[list[int]]) -> list[int]:
n = len(lists)
if n < 2:
return []
counts = Counter()
for sub in lists:
for val in set(sub):
counts[val] += 1
threshold = n - 1
result = sorted(val for val, cnt in count... | 15 | 15 | true | 57.5 | 0 | 0.5 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 0,
"main_function_nloc": 0,
"max_cognitive_complexity": 0,
"max_cyclomatic_complexity": 0,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 0,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 39.863137138648355,
"main_function_nloc": 11,
"max_cognitive_complexity": 7,
"max_cyclomatic_complexity": 6,
"max_fan_out": 0,
"max_maximum_nesting_depth": 2,
"max_nloc": 11,
"max_parameter_count": 1,
"mean_cognitive_complexity"... | e373ba43a35a0c4891bd308c8fb7a2ec40caf8c9afbd13964a262b9a5a9535c3 |
combinatorial_logic_hard_003 | combinatorial_logic | hard | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | from collections import Counter
def integers_in_exactly_two_lists(lists: list[list[int]]) -> list[int]:
if len(lists) < 2:
return []
counts = Counter()
for inner in lists:
for value in set(inner):
counts[value] += 1
result = [value for value, c in counts.items() if c == 2]
... | 15 | 15 | true | 80 | 0.2 | 1 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 0,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 27,
"main_function_nloc": 9,
"max_cognitive_complexity": 7,
"max_cyclomatic_complexity": 6,
"max_fan_out": 0,
"max_maximum_nesting_depth": 2,
"max_nloc": 9,
"max_parameter_count": 1,
"mean_cognitive_complexity": 7,
"mean_cyclo... | 278fe4a7fdef870cbf2e6f154e9262498aa2fd7790fc674585b98016a77ce293 |
combinatorial_logic_hard_004 | combinatorial_logic | medium | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | from collections import Counter
def integers_in_at_least_k_lists(lists: list[list[int]], k: int) -> list[int]:
if not lists or k > len(lists):
return []
counts = Counter()
for inner in lists:
counts.update(set(inner))
result = [num for num, cnt in counts.items() if cnt >= k]
return ... | 15 | 15 | true | 80 | 0.2 | 1 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 0,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 36.541209043760986,
"main_function_nloc": 8,
"max_cognitive_complexity": 6,
"max_cyclomatic_complexity": 6,
"max_fan_out": 0,
"max_maximum_nesting_depth": 2,
"max_nloc": 8,
"max_parameter_count": 2,
"mean_cognitive_complexity": ... | b33de654e872e93b4f56a71c7c4e7a1c83cde79484ccc49ae9fd6264cc661ac8 |
combinatorial_logic_hard_005 | combinatorial_logic | medium | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | import collections
def integers_in_exactly_half_lists(lists: list[list[int]]) -> list[int]:
if lists is None or len(lists) < 2:
return []
half = len(lists) // 2
counts = collections.Counter()
for inner in lists:
for value in set(inner):
counts[value] += 1
result = [value... | 15 | 15 | true | 57.5 | 0 | 0.5 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 0,
"main_function_nloc": 0,
"max_cognitive_complexity": 0,
"max_cyclomatic_complexity": 0,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 0,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 73.57433114250613,
"main_function_nloc": 10,
"max_cognitive_complexity": 8,
"max_cyclomatic_complexity": 7,
"max_fan_out": 0,
"max_maximum_nesting_depth": 2,
"max_nloc": 10,
"max_parameter_count": 1,
"mean_cognitive_complexity":... | 06e722b178bff386957ce949c9e36e7987bd53c9ae905b6c575f75a4d91b3b4a |
combinatorial_logic_hard_006 | combinatorial_logic | medium | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | from collections import defaultdict
def _build_appearance_and_maxes(lists: list[list[int]]) -> tuple[dict[int, set[int]], list[int]]:
appearance: dict[int, set[int]] = defaultdict(set)
maxes: list[int] = []
for idx, inner in enumerate(lists):
if not inner:
continue
maxes.append... | 15 | 15 | true | 55 | 0.4 | 1 | 1 | 0 | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 0,
"main_function_nloc": 1,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 0,
"max_maximum_nesting_depth": 1,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 1,
"coupling_density": 0.5,
"halstead_volume": 11.60964047443681,
"main_function_nloc": 4,
"max_cognitive_complexity": 5,
"max_cyclomatic_complexity": 4,
"max_fan_out": 1,
"max_maximum_nesting_depth": 2,
"max_nloc": 10,
"max_parameter_count": 1,
"mean_cognitive_complexity": 4... | d4f95c5e77dd2dd82e6697018c0079552718eca990318923ca0e99dbf857d0b4 |
combinatorial_logic_hard_007 | combinatorial_logic | medium | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | def integers_not_in_consecutive_lists(lists: list[list[int]]) -> list[int]:
if len(lists) < 2:
return []
all_ints = set()
for lst in lists:
all_ints |= set(lst)
bad = set()
for a, b in zip(lists, lists[1:]):
bad |= set(a) & set(b)
return sorted(all_ints - bad) | 15 | 15 | true | 75 | 0 | 1 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 0,
"main_function_nloc": 0,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 53.77443751081735,
"main_function_nloc": 10,
"max_cognitive_complexity": 3,
"max_cyclomatic_complexity": 4,
"max_fan_out": 0,
"max_maximum_nesting_depth": 1,
"max_nloc": 10,
"max_parameter_count": 1,
"mean_cognitive_complexity":... | c664b0185136b224a923b8b3a8b40828d9c9299cf8f07ffd2998da37a5966eca |
combinatorial_logic_hard_009 | combinatorial_logic | hard | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | import collections
def count_occurrences(sets):
counter = collections.Counter()
for s in sets:
for x in s:
counter[x] += 1
return counter
def find_consecutive(sets):
bad = set()
for i in range(len(sets) - 1):
bad |= sets[i] & sets[i + 1]
return bad
def nonconsecuti... | 15 | 15 | true | 33.75 | 0.6 | 0.25 | 1 | 0 | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 0,
"main_function_nloc": 1,
"max_cognitive_complexity": 0,
"max_cyclomatic_complexity": 0,
"max_fan_out": 0,
"max_maximum_nesting_depth": 0,
"max_nloc": 1,
"max_parameter_count": 1,
"mean_cognitive_complexity": 0,
"mean_cyclom... | {
"call_graph_depth": 1,
"coupling_density": 0.3333333333333333,
"halstead_volume": 123.79398751947124,
"main_function_nloc": 8,
"max_cognitive_complexity": 6,
"max_cyclomatic_complexity": 6,
"max_fan_out": 2,
"max_maximum_nesting_depth": 2,
"max_nloc": 8,
"max_parameter_count": 1,
"mean_cognitive... | 7df8621bc6878befd9f08e1eec2e919e1bef45f012496ea7d1c24317170bc618 |
combinatorial_logic_hard_010 | combinatorial_logic | medium | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | 0 | 15 | false | 0 | 0 | 0 | 0 | 0 | {
"call_graph_depth": null,
"coupling_density": null,
"halstead_volume": null,
"main_function_nloc": null,
"max_cognitive_complexity": null,
"max_cyclomatic_complexity": null,
"max_fan_out": null,
"max_maximum_nesting_depth": null,
"max_nloc": null,
"max_parameter_count": null,
"mean_cognitive_com... | {
"call_graph_depth": null,
"coupling_density": null,
"halstead_volume": null,
"main_function_nloc": null,
"max_cognitive_complexity": null,
"max_cyclomatic_complexity": null,
"max_fan_out": null,
"max_maximum_nesting_depth": null,
"max_nloc": null,
"max_parameter_count": null,
"mean_cognitive_com... | e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855 | |
combinatorial_logic_medium_001 | combinatorial_logic | medium | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | from collections import Counter
def intersection_of_two_or_more(lst1: list[int], lst2: list[int], lst3: list[int]) -> list[int]:
s1, s2, s3 = set(lst1), set(lst2), set(lst3)
combined = list(s1) + list(s2) + list(s3)
counts = Counter(combined)
result = [num for num, cnt in counts.items() if cnt >= 2]
... | 15 | 15 | true | 53.75 | 0.2 | 0.25 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 0,
"max_cognitive_complexity": 0,
"max_cyclomatic_complexity": 0,
"max_fan_out": 1,
"max_maximum_nesting_depth": 0,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 0,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 27,
"main_function_nloc": 6,
"max_cognitive_complexity": 3,
"max_cyclomatic_complexity": 3,
"max_fan_out": 0,
"max_maximum_nesting_depth": 2,
"max_nloc": 6,
"max_parameter_count": 3,
"mean_cognitive_complexity": 3,
"mean_cyclo... | ce5d2ac34888368e7d949ba9c087e6dcdb60d1305c1d60827a48e620fbe40fec |
combinatorial_logic_medium_002 | combinatorial_logic | easy | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | def unique_to_first_list(lst1: list[int], lst2: list[int], lst3: list[int]) -> list[int]:
set1 = set(lst1)
exclude = set(lst2) | set(lst3)
result = set1 - exclude
return sorted(result) | 15 | 15 | true | 75 | 0 | 1 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 0,
"main_function_nloc": 0,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 15.509775004326936,
"main_function_nloc": 5,
"max_cognitive_complexity": 0,
"max_cyclomatic_complexity": 1,
"max_fan_out": 0,
"max_maximum_nesting_depth": 0,
"max_nloc": 5,
"max_parameter_count": 3,
"mean_cognitive_complexity": ... | 084e4ecadaa05b71d43e824f2200fa1ec278f0a63182b9e3ccc52e67187d3b3f |
combinatorial_logic_medium_003 | combinatorial_logic | easy | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | def common_elements_all(lists: list[list[int]]) -> list[int]:
if not lists or any(len(l) == 0 for l in lists):
return []
sets = [set(l) for l in lists]
common = set.intersection(*sets)
return sorted(common) | 15 | 15 | true | 68.75 | 0.8 | 0.25 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 0,
"main_function_nloc": 1,
"max_cognitive_complexity": 0,
"max_cyclomatic_complexity": 0,
"max_fan_out": 1,
"max_maximum_nesting_depth": 0,
"max_nloc": 1,
"max_parameter_count": 1,
"mean_cognitive_complexity": 0,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 24,
"main_function_nloc": 6,
"max_cognitive_complexity": 5,
"max_cyclomatic_complexity": 5,
"max_fan_out": 0,
"max_maximum_nesting_depth": 2,
"max_nloc": 6,
"max_parameter_count": 1,
"mean_cognitive_complexity": 5,
"mean_cyclo... | 1e947233b790ef098372c651c966b451912698045b187bd629347da70300cc40 |
combinatorial_logic_medium_004 | combinatorial_logic | medium | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | from collections import Counter
def unique_to_one_list(lists: list[list[int]]) -> list[int]:
counts = Counter()
for inner in lists:
counts.update(set(inner))
result = [num for num, cnt in counts.items() if cnt == 1]
return sorted(result) | 15 | 15 | true | 80 | 0.2 | 1 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 0,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 4.754887502163469,
"main_function_nloc": 6,
"max_cognitive_complexity": 4,
"max_cyclomatic_complexity": 4,
"max_fan_out": 0,
"max_maximum_nesting_depth": 2,
"max_nloc": 6,
"max_parameter_count": 1,
"mean_cognitive_complexity": 4... | 07590d1a1194d373e85806feaca8cad4c837533852e38a1f3bffe5ad793c4318 |
combinatorial_logic_medium_006 | combinatorial_logic | easy | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | import functools
def integers_in_all_but_first(lists: list[list[int]]) -> list[int]:
if len(lists) < 2:
return []
sets = (set(lst) for lst in lists[1:])
return sorted(functools.reduce(lambda a, b: a & b, sets)) | 15 | 15 | true | 95 | 0.8 | 1 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 0,
"main_function_nloc": 1,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 1,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 15.509775004326936,
"main_function_nloc": 5,
"max_cognitive_complexity": 2,
"max_cyclomatic_complexity": 3,
"max_fan_out": 0,
"max_maximum_nesting_depth": 1,
"max_nloc": 5,
"max_parameter_count": 1,
"mean_cognitive_complexity": ... | b732b053dcf7268a9ec64c659e7228a63a861719356c2cf3b2ac101da9c8bc7c |
combinatorial_logic_medium_007 | combinatorial_logic | easy | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | def first_list_unique_difference(lists: list[list[int]]) -> list[int]:
if len(lists) < 2 or not lists[0]:
return []
others = set().union(*lists[1:])
return sorted(set(lists[0]) - others) | 15 | 15 | true | 95 | 0.8 | 1 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 0,
"main_function_nloc": 1,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 1,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 38.053747805010275,
"main_function_nloc": 5,
"max_cognitive_complexity": 2,
"max_cyclomatic_complexity": 3,
"max_fan_out": 0,
"max_maximum_nesting_depth": 1,
"max_nloc": 5,
"max_parameter_count": 1,
"mean_cognitive_complexity": ... | d3defaafbbe19502e0c898c83a9ec18db143e53e907fa198178ef49cb44080dd |
combinatorial_logic_medium_010 | combinatorial_logic | easy | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | def unique_even_index_only(lists: list[list[int]]) -> list[int]:
evens = [set(l) for i, l in enumerate(lists) if i % 2 == 0]
odds = [set(l) for i, l in enumerate(lists) if i % 2 == 1]
if not evens:
return []
common = set.intersection(*evens)
excluded = set.union(*odds) if odds else set()
... | 15 | 15 | true | 48.75 | 0 | 0.25 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 0,
"main_function_nloc": 0,
"max_cognitive_complexity": 0,
"max_cyclomatic_complexity": 0,
"max_fan_out": 1,
"max_maximum_nesting_depth": 0,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 0,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 62.907475208398566,
"main_function_nloc": 8,
"max_cognitive_complexity": 8,
"max_cyclomatic_complexity": 7,
"max_fan_out": 0,
"max_maximum_nesting_depth": 2,
"max_nloc": 8,
"max_parameter_count": 1,
"mean_cognitive_complexity": ... | 27793590f3e2af593476b62e9b42c5fdd1c72c9bb991002d060e681e4d092834 |
coordinate_geometry_easy_001 | coordinate_geometry | easy | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | def manhattan_distance(p1: list[int], p2: list[int]) -> int:
x1, y1 = p1
x2, y2 = p2
return abs(x1 - x2) + abs(y1 - y2) | 15 | 15 | true | 80 | 0.2 | 1 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 0,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 27,
"main_function_nloc": 4,
"max_cognitive_complexity": 0,
"max_cyclomatic_complexity": 1,
"max_fan_out": 0,
"max_maximum_nesting_depth": 0,
"max_nloc": 4,
"max_parameter_count": 2,
"mean_cognitive_complexity": 0,
"mean_cyclo... | 6fa932a844cbcc2e640a85065cdf03d92903e840cb95201657b978d2d29e297c |
coordinate_geometry_easy_002 | coordinate_geometry | easy | EP only | anthropic/claude-sonnet-5 | large | baseline | def are_points_collinear_with_origin(a: list[int], b: list[int]) -> bool:
ax, ay = a
bx, by = b
return ax * by - ay * bx == 0 | 15 | 15 | true | 75 | 0 | 1 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 0,
"main_function_nloc": 0,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 41.51317942364757,
"main_function_nloc": 4,
"max_cognitive_complexity": 0,
"max_cyclomatic_complexity": 1,
"max_fan_out": 0,
"max_maximum_nesting_depth": 0,
"max_nloc": 4,
"max_parameter_count": 2,
"mean_cognitive_complexity": 0... | 15ef3a8f09db10e260baa492402f212666d98c271fe2b95c98f94cf0ad9aff0e |
coordinate_geometry_easy_003 | coordinate_geometry | easy | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | def is_axis_parallel(point1: list[int], point2: list[int]) -> bool:
x1, y1 = point1
x2, y2 = point2
return x1 == x2 or y1 == y2 | 15 | 15 | true | 80 | 0.2 | 1 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 0,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 27,
"main_function_nloc": 4,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 2,
"max_fan_out": 0,
"max_maximum_nesting_depth": 0,
"max_nloc": 4,
"max_parameter_count": 2,
"mean_cognitive_complexity": 1,
"mean_cyclo... | 3a81f6fb8c5959d86d4d81d8e88e692d9bbe1cdf47866b89d1590ea48249d081 |
coordinate_geometry_easy_004 | coordinate_geometry | easy | EP only | anthropic/claude-sonnet-5 | large | baseline | def midpoint(point1: list[int], point2: list[int]) -> list[int]:
mx = (point1[0] + point2[0]) // 2
my = (point1[1] + point2[1]) // 2
return [mx, my] | 15 | 15 | true | 80 | 0.2 | 1 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 0,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 38.03910001730775,
"main_function_nloc": 4,
"max_cognitive_complexity": 0,
"max_cyclomatic_complexity": 1,
"max_fan_out": 0,
"max_maximum_nesting_depth": 0,
"max_nloc": 4,
"max_parameter_count": 2,
"mean_cognitive_complexity": 0... | 384012b2d8329b9581c707c0c20aea12371f6cf58e5df1e7d8dc3dcd5b9c77c7 |
coordinate_geometry_easy_005 | coordinate_geometry | easy | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | def is_on_axis(point: list[int]) -> bool:
x, y = point
return x == 0 or y == 0 | 15 | 15 | true | 80 | 0.2 | 1 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 0,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 25.26619429851844,
"main_function_nloc": 3,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 2,
"max_fan_out": 0,
"max_maximum_nesting_depth": 0,
"max_nloc": 3,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1... | e1df7dfd3658552c6f58634017e7c1feb60124fb0b8f9ad9a579d1cf3794ef92 |
coordinate_geometry_easy_007 | coordinate_geometry | hard | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | import math
def _subtract(a: list[int], b: list[int]) -> tuple[int, int]:
return (a[0] - b[0], a[1] - b[1])
def _dot(u: tuple[int, int], v: tuple[int, int]) -> int:
return u[0] * v[0] + u[1] * v[1]
def _cross(u: tuple[int, int], v: tuple[int, int]) -> int:
return u[0] * v[1] - u[1] * v[0]
def _is_va... | 15 | 15 | true | 46.875 | 0.6 | 0.625 | 1 | 0 | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 1,
"max_cognitive_complexity": 0,
"max_cyclomatic_complexity": 1,
"max_fan_out": 0,
"max_maximum_nesting_depth": 0,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 2,
"coupling_density": 0.2,
"halstead_volume": 263.6661750735579,
"main_function_nloc": 4,
"max_cognitive_complexity": 3,
"max_cyclomatic_complexity": 4,
"max_fan_out": 2,
"max_maximum_nesting_depth": 1,
"max_nloc": 11,
"max_parameter_count": 3,
"mean_cognitive_complexity": 1... | 99f075fb6199cee3d0b6a861ac5954be2c9a4423e96bf03be7701bcb3433cbbf |
coordinate_geometry_easy_008 | coordinate_geometry | medium | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | def segment_passes_through_origin(p1: list[int], p2: list[int]) -> bool:
x1, y1 = p1
x2, y2 = p2
cross_product_zero = (x1 * y2 - x2 * y1) == 0
dot_product_non_positive = (x1 * x2 + y1 * y2) <= 0
return cross_product_zero and dot_product_non_positive | 15 | 15 | true | 75 | 0 | 1 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 0,
"main_function_nloc": 0,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 114.6940428629768,
"main_function_nloc": 6,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 2,
"max_fan_out": 0,
"max_maximum_nesting_depth": 0,
"max_nloc": 6,
"max_parameter_count": 2,
"mean_cognitive_complexity": 1... | b1964489d1344e30568e2277acd08fc151b8bb2bda616f008622c76bae7e77db |
coordinate_geometry_easy_009 | coordinate_geometry | medium | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | def are_points_equidistant_from_origin(p1: list[int], p2: list[int]) -> bool:
d1 = p1[0] ** 2 + p1[1] ** 2
d2 = p2[0] ** 2 + p2[1] ** 2
return d1 == d2 | 15 | 15 | true | 80 | 0.2 | 1 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 0,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 79.95445336320968,
"main_function_nloc": 4,
"max_cognitive_complexity": 0,
"max_cyclomatic_complexity": 1,
"max_fan_out": 0,
"max_maximum_nesting_depth": 0,
"max_nloc": 4,
"max_parameter_count": 2,
"mean_cognitive_complexity": 0... | 15f86a3f225bd99050387fee354fd434f81b6edee6f4caa33c676950b45a2e0f |
coordinate_geometry_hard_001 | coordinate_geometry | hard | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | import itertools
def cross(o, a, b):
return (a[0] - o[0]) * (b[1] - o[1]) - (a[1] - o[1]) * (b[0] - o[0])
def convex_hull(pts):
pts = sorted(set(map(tuple, pts)))
if len(pts) < 3:
return pts
lower = []
for p in pts:
while len(lower) >= 2 and cross(lower[-2], lower[-1], p) <= 0:
... | 15 | 15 | true | 62.5 | 0.2 | 0.5 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": 1,
"halstead_volume": 0,
"main_function_nloc": 1,
"max_cognitive_complexity": 0,
"max_cyclomatic_complexity": 0,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 0,
"mean_cyclomati... | {
"call_graph_depth": 2,
"coupling_density": 0.3333333333333333,
"halstead_volume": 339.7159976350619,
"main_function_nloc": 6,
"max_cognitive_complexity": 9,
"max_cyclomatic_complexity": 8,
"max_fan_out": 1,
"max_maximum_nesting_depth": 2,
"max_nloc": 15,
"max_parameter_count": 3,
"mean_cognitive... | e4cc9524b71dc69508ee212898b0123be807c18c44b7e37e94565ea7a0f802c9 |
coordinate_geometry_hard_003 | coordinate_geometry | hard | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | 0 | 15 | false | 0 | 0 | 0 | 0 | 0 | {
"call_graph_depth": null,
"coupling_density": null,
"halstead_volume": null,
"main_function_nloc": null,
"max_cognitive_complexity": null,
"max_cyclomatic_complexity": null,
"max_fan_out": null,
"max_maximum_nesting_depth": null,
"max_nloc": null,
"max_parameter_count": null,
"mean_cognitive_com... | {
"call_graph_depth": null,
"coupling_density": null,
"halstead_volume": null,
"main_function_nloc": null,
"max_cognitive_complexity": null,
"max_cyclomatic_complexity": null,
"max_fan_out": null,
"max_maximum_nesting_depth": null,
"max_nloc": null,
"max_parameter_count": null,
"mean_cognitive_com... | e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855 | |
coordinate_geometry_hard_004 | coordinate_geometry | hard | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | def _cross(o: list[int], a: list[int], b: list[int]) -> int:
return (a[0] - o[0]) * (b[1] - o[1]) - (a[1] - o[1]) * (b[0] - o[0])
def segments_properly_intersect(seg1: list[int], seg2: list[int]) -> bool:
p1, p2 = (seg1[0], seg1[1]), (seg1[2], seg1[3])
p3, p4 = (seg2[0], seg2[1]), (seg2[2], seg2[3])
d... | 15 | 15 | true | 55 | 0.8 | 1 | 0 | 0 | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 1,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 0,
"max_maximum_nesting_depth": 1,
"max_nloc": 1,
"max_parameter_count": 0,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 1,
"coupling_density": 0.5,
"halstead_volume": 171.1759500778849,
"main_function_nloc": 8,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 2,
"max_fan_out": 1,
"max_maximum_nesting_depth": 0,
"max_nloc": 8,
"max_parameter_count": 3,
"mean_cognitive_complexity": 0.... | 1a68f6dc71160963bf859c55e72d56ecfb1cb6bc32854fc275fb71d7127e1d35 |
coordinate_geometry_hard_005 | coordinate_geometry | medium | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | from itertools import combinations
from fractions import Fraction
def _line_key(p1, p2):
x1, y1 = p1
x2, y2 = p2
dx = x2 - x1
dy = y2 - y1
if dx == 0 or dy == 0:
return None
slope = Fraction(dy, dx)
if slope <= 0:
return None
intercept = Fraction(y1) - slope * Fraction(... | 15 | 15 | true | 31.25 | 0.2 | 0.75 | 0 | 0 | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 0,
"main_function_nloc": 1,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 0,
"max_fan_out": 0,
"max_maximum_nesting_depth": 1,
"max_nloc": 0,
"max_parameter_count": 0,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 1,
"coupling_density": 0.5,
"halstead_volume": 118.59257041502654,
"main_function_nloc": 7,
"max_cognitive_complexity": 3,
"max_cyclomatic_complexity": 4,
"max_fan_out": 1,
"max_maximum_nesting_depth": 2,
"max_nloc": 12,
"max_parameter_count": 2,
"mean_cognitive_complexity": ... | aff248f4c631dbed3a0ec908323da2ecb9963afdbf62967a5a768e41228b383e |
coordinate_geometry_hard_007 | coordinate_geometry | medium | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | from itertools import combinations
def _area2(a, b, c):
return (b[0] - a[0]) * (c[1] - a[1]) - (b[1] - a[1]) * (c[0] - a[0])
def count_distinct_triangles(points: list[list[int]]) -> int:
count = 0
for a, b, c in combinations(points, 3):
if _area2(a, b, c) != 0:
count += 1
return co... | 15 | 15 | true | 100 | 1 | 1 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": 1,
"halstead_volume": 1,
"main_function_nloc": 1,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 1,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclomati... | {
"call_graph_depth": 1,
"coupling_density": 0.5,
"halstead_volume": 120.40465370320703,
"main_function_nloc": 6,
"max_cognitive_complexity": 3,
"max_cyclomatic_complexity": 3,
"max_fan_out": 1,
"max_maximum_nesting_depth": 2,
"max_nloc": 6,
"max_parameter_count": 3,
"mean_cognitive_complexity": 1... | 6f0171f7b6100ef6945d31406617e19233cf61a7aadedb8a2f46753f942ba35c |
coordinate_geometry_hard_008 | coordinate_geometry | hard | EP only | anthropic/claude-sonnet-5 | large | baseline | from itertools import combinations
def _cross(o, a, b):
return (a[0]-o[0])*(b[1]-o[1]) - (a[1]-o[1])*(b[0]-o[0])
def _is_collinear(p1, p2, p3, p4):
return _cross(p1, p2, p3) == 0 and _cross(p1, p2, p4) == 0
def count_parallelograms(points: list[list[int]]) -> int:
groups = {}
for p1, p2 in combinatio... | 15 | 15 | true | 46.875 | 0.2 | 0.625 | 0.5 | 0.5 | {
"call_graph_depth": 0,
"coupling_density": 1,
"halstead_volume": 0,
"main_function_nloc": 0,
"max_cognitive_complexity": 0,
"max_cyclomatic_complexity": 0,
"max_fan_out": 1,
"max_maximum_nesting_depth": 0,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclomati... | {
"call_graph_depth": 2,
"coupling_density": 0.3333333333333333,
"halstead_volume": 242.98647506778866,
"main_function_nloc": 12,
"max_cognitive_complexity": 8,
"max_cyclomatic_complexity": 6,
"max_fan_out": 1,
"max_maximum_nesting_depth": 3,
"max_nloc": 12,
"max_parameter_count": 4,
"mean_cogniti... | ae1b3b316a8c437559370ec91512a5c5f922098a5f803b91e453c744b09ed0df |
coordinate_geometry_hard_009 | coordinate_geometry | hard | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | from itertools import combinations
def _cross(o, a, b):
return (a[0] - o[0]) * (b[1] - o[1]) - (a[1] - o[1]) * (b[0] - o[0])
def _convex_hull(pts):
pts = sorted(pts)
if len(pts) < 3:
return pts
lower = []
for p in pts:
while len(lower) >= 2 and _cross(lower[-2], lower[-1], p) <= ... | 15 | 15 | true | 53.125 | 0 | 0.375 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": 1,
"halstead_volume": 0,
"main_function_nloc": 0,
"max_cognitive_complexity": 0,
"max_cyclomatic_complexity": 0,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 0,
"mean_cyclomati... | {
"call_graph_depth": 2,
"coupling_density": 0.3333333333333333,
"halstead_volume": 339.7159976350619,
"main_function_nloc": 7,
"max_cognitive_complexity": 9,
"max_cyclomatic_complexity": 8,
"max_fan_out": 1,
"max_maximum_nesting_depth": 2,
"max_nloc": 15,
"max_parameter_count": 3,
"mean_cognitive... | e875cdaf977e6291a6f078cef93378c79db4aba1b2121243a1f5f75852fc4ff4 |
coordinate_geometry_medium_001 | coordinate_geometry | easy | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | def intersection_area(rect1: list[int], rect2: list[int]) -> int:
x1 = max(rect1[0], rect2[0])
y1 = max(rect1[1], rect2[1])
x2 = min(rect1[2], rect2[2])
y2 = min(rect1[3], rect2[3])
if x2 <= x1 or y2 <= y1:
return 0
return (x2 - x1) * (y2 - y1) | 15 | 15 | true | 48.75 | 0 | 0.25 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 0,
"main_function_nloc": 0,
"max_cognitive_complexity": 0,
"max_cyclomatic_complexity": 0,
"max_fan_out": 1,
"max_maximum_nesting_depth": 0,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 0,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 64.52932501298082,
"main_function_nloc": 8,
"max_cognitive_complexity": 2,
"max_cyclomatic_complexity": 3,
"max_fan_out": 0,
"max_maximum_nesting_depth": 1,
"max_nloc": 8,
"max_parameter_count": 2,
"mean_cognitive_complexity": 2... | 3f30bf65e630f2d22e5361723ab4485228f0d9011cbabe4e87f1595997b79929 |
coordinate_geometry_medium_002 | coordinate_geometry | medium | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | def rectangles_overlap(rect1: list[int], rect2: list[int]) -> bool:
x1, y1, x2, y2 = rect1
a1, b1, a2, b2 = rect2
overlap_x = min(x2, a2) - max(x1, a1)
overlap_y = min(y2, b2) - max(y1, b1)
return overlap_x > 0 and overlap_y > 0 | 15 | 15 | true | 100 | 1 | 1 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 1,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 1,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 53.77443751081735,
"main_function_nloc": 6,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 2,
"max_fan_out": 0,
"max_maximum_nesting_depth": 0,
"max_nloc": 6,
"max_parameter_count": 2,
"mean_cognitive_complexity": 1... | 2bd6f4e68baffda608953f6303c473d9be36e367565f0296d7891000ee5ec5b4 |
coordinate_geometry_medium_004 | coordinate_geometry | hard | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | def are_circles_tangent(c1: list[int], c2: list[int]) -> bool:
x1, y1, r1 = c1
x2, y2, r2 = c2
d_sq = (x1 - x2) ** 2 + (y1 - y2) ** 2
sum_sq = (r1 + r2) ** 2
diff_sq = (r1 - r2) ** 2
return d_sq == sum_sq or d_sq == diff_sq | 14 | 15 | false | 0 | 0.2 | 1 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 0,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 162.84823041805248,
"main_function_nloc": 7,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 2,
"max_fan_out": 0,
"max_maximum_nesting_depth": 0,
"max_nloc": 7,
"max_parameter_count": 2,
"mean_cognitive_complexity": ... | 0e900a029d7cb0c6ee0c431dce3eec86630b2b0cebe12a11683b35363891cb8d |
coordinate_geometry_medium_005 | coordinate_geometry | hard | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | def _sq_dist(a: list[int], b: list[int]) -> int:
return (a[0] - b[0]) ** 2 + (a[1] - b[1]) ** 2
def _is_collinear(p1: list[int], p2: list[int], p3: list[int]) -> bool:
return (p2[0] - p1[0]) * (p3[1] - p1[1]) - (p2[1] - p1[1]) * (p3[0] - p1[0]) == 0
def is_isosceles_triangle(p1: list[int], p2: list[int], p3... | 15 | 15 | true | 32.5 | 0.2 | 0.5 | 1 | 0 | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 0,
"main_function_nloc": 0,
"max_cognitive_complexity": 0,
"max_cyclomatic_complexity": 0,
"max_fan_out": 0,
"max_maximum_nesting_depth": 1,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 0,
"mean_cyclom... | {
"call_graph_depth": 1,
"coupling_density": 0.3333333333333333,
"halstead_volume": 352.7072090556364,
"main_function_nloc": 9,
"max_cognitive_complexity": 4,
"max_cyclomatic_complexity": 7,
"max_fan_out": 2,
"max_maximum_nesting_depth": 1,
"max_nloc": 9,
"max_parameter_count": 3,
"mean_cognitive_... | da1206ba4aece79d38dfa45871dd91370b6701cbd4fd8228c3229891e4dacdaa |
coordinate_geometry_medium_007 | coordinate_geometry | easy | EP only | anthropic/claude-sonnet-5 | large | baseline | def is_axis_aligned_rectangle_vertex_triple(p1: list[int], p2: list[int], p3: list[int]) -> bool:
xs = {p1[0], p2[0], p3[0]}
ys = {p1[1], p2[1], p3[1]}
return len(xs) == 2 and len(ys) == 2 | 15 | 15 | true | 100 | 1 | 1 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 1,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 1,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 25.26619429851844,
"main_function_nloc": 4,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 2,
"max_fan_out": 0,
"max_maximum_nesting_depth": 0,
"max_nloc": 4,
"max_parameter_count": 3,
"mean_cognitive_complexity": 1... | b7fc861baf4fa831792686a7859d3dc29eb9445d9ab458ace2e669648711eaf4 |
coordinate_geometry_medium_008 | coordinate_geometry | medium | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | import math
def circle_strict_containment(c1: list[int], c2: list[int]) -> bool:
x1, y1, r1 = c1
x2, y2, r2 = c2
d = math.hypot(x1 - x2, y1 - y2)
if r1 == r2 and d == 0:
return False
return d + min(r1, r2) < max(r1, r2) | 15 | 15 | true | 53.75 | 0.2 | 0.25 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 0,
"max_cognitive_complexity": 0,
"max_cyclomatic_complexity": 0,
"max_fan_out": 1,
"max_maximum_nesting_depth": 0,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 0,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 87.56842503028855,
"main_function_nloc": 7,
"max_cognitive_complexity": 2,
"max_cyclomatic_complexity": 3,
"max_fan_out": 0,
"max_maximum_nesting_depth": 1,
"max_nloc": 7,
"max_parameter_count": 2,
"mean_cognitive_complexity": 2... | 00a924c088c71296e9ce7dd4d8640c311847426befd98fd05747850f833362bf |
coordinate_geometry_medium_009 | coordinate_geometry | easy | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | def enclosing_rectangle(rect1: list[int], rect2: list[int]) -> list[int]:
x1, y1, x2, y2 = rect1
x3, y3, x4, y4 = rect2
min_x = min(x1, x3)
min_y = min(y1, y3)
max_x = max(x2, x4)
max_y = max(y2, y4)
return [min_x, min_y, max_x, max_y] | 15 | 15 | true | 80 | 0.2 | 1 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 0,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 0,
"main_function_nloc": 8,
"max_cognitive_complexity": 0,
"max_cyclomatic_complexity": 1,
"max_fan_out": 0,
"max_maximum_nesting_depth": 0,
"max_nloc": 8,
"max_parameter_count": 2,
"mean_cognitive_complexity": 0,
"mean_cyclom... | 4363c95516ee31c3c8b73b65d2ed02a48882e0e509196140a02a5bc6f71bbda4 |
counting_and_aggregation_easy_001 | counting_and_aggregation | easy | EP only | anthropic/claude-sonnet-5 | large | baseline | def count_unique_evens(numbers: list[int]) -> int:
evens = set(n for n in numbers if n % 2 == 0)
return len(evens) | 15 | 15 | true | 80 | 0.2 | 1 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 0,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 15.509775004326936,
"main_function_nloc": 3,
"max_cognitive_complexity": 3,
"max_cyclomatic_complexity": 3,
"max_fan_out": 0,
"max_maximum_nesting_depth": 2,
"max_nloc": 3,
"max_parameter_count": 1,
"mean_cognitive_complexity": ... | 5c7469950653d4fe7ef3be1d1973bf570155968ad2776f7ac369fc283f663a83 |
counting_and_aggregation_easy_002 | counting_and_aggregation | easy | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | def count_length_three(strings: list[str]) -> int:
return sum(1 for s in strings if len(s) == 3) | 15 | 15 | true | 73.75 | 1 | 0.25 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 1,
"max_cognitive_complexity": 0,
"max_cyclomatic_complexity": 0,
"max_fan_out": 1,
"max_maximum_nesting_depth": 0,
"max_nloc": 1,
"max_parameter_count": 1,
"mean_cognitive_complexity": 0,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 4.754887502163469,
"main_function_nloc": 2,
"max_cognitive_complexity": 3,
"max_cyclomatic_complexity": 3,
"max_fan_out": 0,
"max_maximum_nesting_depth": 2,
"max_nloc": 2,
"max_parameter_count": 1,
"mean_cognitive_complexity": 3... | 13769424016815657ab6a3dbbfd26fb7d34a8c954650582a807710588232130f |
counting_and_aggregation_easy_003 | counting_and_aggregation | easy | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | from collections import Counter
def sum_duplicates(nums: list[int]) -> int:
counts = Counter(nums)
return sum(n for n in nums if counts[n] > 1) | 15 | 15 | true | 100 | 1 | 1 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 1,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 1,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 4.754887502163469,
"main_function_nloc": 3,
"max_cognitive_complexity": 3,
"max_cyclomatic_complexity": 3,
"max_fan_out": 0,
"max_maximum_nesting_depth": 2,
"max_nloc": 3,
"max_parameter_count": 1,
"mean_cognitive_complexity": 3... | 373aba2a49e0f7bbf102de068ae408eb2400c30418a4c51f339d4e91955a2c41 |
counting_and_aggregation_easy_004 | counting_and_aggregation | easy | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | def count_a_starting_chars(strings: list[str]) -> int:
total = 0
for s in strings:
if s and s[0].lower() == 'a':
total += len(s)
return total | 15 | 15 | true | 57.5 | 0 | 0.5 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 0,
"main_function_nloc": 0,
"max_cognitive_complexity": 0,
"max_cyclomatic_complexity": 0,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 0,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 28.529325012980813,
"main_function_nloc": 6,
"max_cognitive_complexity": 4,
"max_cyclomatic_complexity": 4,
"max_fan_out": 0,
"max_maximum_nesting_depth": 2,
"max_nloc": 6,
"max_parameter_count": 1,
"mean_cognitive_complexity": ... | f9738a5434fa9818f21a012ca7fd41762a0e42ee5fb792f73b5769340fb69bef |
counting_and_aggregation_easy_006 | counting_and_aggregation | easy | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | def _count_increasing(nums: list[int]) -> int:
count = 0
max_val = None
for n in nums:
if max_val is None or n > max_val:
count += 1
max_val = n
return count
def count_strictly_increasing(nums: list[int]) -> int:
return _count_increasing(nums) | 15 | 15 | true | 100 | 1 | 1 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": 1,
"halstead_volume": 1,
"main_function_nloc": 1,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 1,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclomati... | {
"call_graph_depth": 1,
"coupling_density": 0.5,
"halstead_volume": 41.51317942364757,
"main_function_nloc": 2,
"max_cognitive_complexity": 4,
"max_cyclomatic_complexity": 4,
"max_fan_out": 1,
"max_maximum_nesting_depth": 2,
"max_nloc": 8,
"max_parameter_count": 1,
"mean_cognitive_complexity": 2,... | 81dbe959b1da44631569ff5c879927edba5bfc2d9d3180320fd4dc2b82dd9a3b |
counting_and_aggregation_easy_007 | counting_and_aggregation | hard | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | from collections import Counter
def count_common_vowels(strings: list[str]) -> int:
vowel_sets = [set(c for c in s.lower() if c in "aeiou") for s in strings]
counts = Counter(v for vs in vowel_sets for v in vs)
return sum(1 for v, c in counts.items() if c >= 2) | 15 | 15 | true | 53.75 | 0.2 | 0.25 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 0,
"max_cognitive_complexity": 0,
"max_cyclomatic_complexity": 0,
"max_fan_out": 1,
"max_maximum_nesting_depth": 0,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 0,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 13.931568569324174,
"main_function_nloc": 4,
"max_cognitive_complexity": 12,
"max_cyclomatic_complexity": 8,
"max_fan_out": 0,
"max_maximum_nesting_depth": 3,
"max_nloc": 4,
"max_parameter_count": 1,
"mean_cognitive_complexity":... | 49c3b175526727f9a9aab441e31c357a112a7f6db0c836480fef97c0a9a95d69 |
counting_and_aggregation_easy_008 | counting_and_aggregation | easy | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | def _has_repeat(s: str) -> bool:
return len(set(s)) != len(s)
def count_strings_with_repeats(strings: list[str]) -> int:
return sum(1 for s in strings if _has_repeat(s)) | 15 | 15 | true | 47.5 | 0.8 | 0.5 | 1 | 0 | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 1,
"max_cognitive_complexity": 0,
"max_cyclomatic_complexity": 0,
"max_fan_out": 0,
"max_maximum_nesting_depth": 0,
"max_nloc": 1,
"max_parameter_count": 1,
"mean_cognitive_complexity": 0,
"mean_cyclom... | {
"call_graph_depth": 1,
"coupling_density": 0.5,
"halstead_volume": 4.754887502163469,
"main_function_nloc": 2,
"max_cognitive_complexity": 3,
"max_cyclomatic_complexity": 3,
"max_fan_out": 1,
"max_maximum_nesting_depth": 2,
"max_nloc": 2,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1.... | 8d2ec5cbe6efe4b7a3e5d98286432684c273bc82cb37aa232f931fb4a1ac72f7 |
counting_and_aggregation_easy_009 | counting_and_aggregation | easy | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | def count_total_e(strings: list[str]) -> int:
total = 0
for s in strings:
total += s.lower().count('e')
return total | 15 | 15 | true | 80 | 0.2 | 1 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 0,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 4.754887502163469,
"main_function_nloc": 5,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 2,
"max_fan_out": 0,
"max_maximum_nesting_depth": 1,
"max_nloc": 5,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1... | bd0798a50529238ad3b4a23398815a42e3a12275429599f4511044fcf816c8c2 |
counting_and_aggregation_easy_010 | counting_and_aggregation | easy | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | from collections import Counter
def unique_long_strings(strings: list[str]) -> list[str]:
counts = Counter(strings)
result = [s for s in set(strings) if counts[s] == 1 and len(s) > 3]
return sorted(result) | 15 | 15 | true | 80 | 0.2 | 1 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 0,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 28.529325012980813,
"main_function_nloc": 4,
"max_cognitive_complexity": 4,
"max_cyclomatic_complexity": 4,
"max_fan_out": 0,
"max_maximum_nesting_depth": 2,
"max_nloc": 4,
"max_parameter_count": 1,
"mean_cognitive_complexity": ... | bf18c2ba115094efcd30c1c3ae02bbcc034a899964db64f66e425883f374d06f |
counting_and_aggregation_hard_001 | counting_and_aggregation | hard | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | from collections import Counter
def is_prime(n: int) -> bool:
if n < 2:
return False
if n < 4:
return True
if n % 2 == 0:
return False
for i in range(3, int(n ** 0.5) + 1, 2):
if n % i == 0:
return False
return True
def keys_with_prime_aggregate_sum(di... | 15 | 15 | true | 95 | 0.8 | 1 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": 1,
"halstead_volume": 1,
"main_function_nloc": 0,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 1,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclomati... | {
"call_graph_depth": 1,
"coupling_density": 0.5,
"halstead_volume": 110.36149671375918,
"main_function_nloc": 7,
"max_cognitive_complexity": 6,
"max_cyclomatic_complexity": 6,
"max_fan_out": 1,
"max_maximum_nesting_depth": 2,
"max_nloc": 11,
"max_parameter_count": 1,
"mean_cognitive_complexity": ... | 1a869fe3f4ce717f31ea0267a73a6bb4ba65534d5fab2598f5dfffcf112d8eb2 |
counting_and_aggregation_hard_002 | counting_and_aggregation | hard | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | from collections import Counter
def _bigrams_in(s: str) -> set:
return {s[i:i + 2] for i in range(len(s) - 1)}
def common_bigrams(strings: list[str]) -> list[str]:
counts = Counter()
for s in strings:
counts.update(_bigrams_in(s))
result = [bg for bg, c in counts.items() if c >= 3]
retur... | 15 | 15 | true | 65 | 0.8 | 1 | 1 | 0 | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 1,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 0,
"max_maximum_nesting_depth": 1,
"max_nloc": 1,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 1,
"coupling_density": 0.5,
"halstead_volume": 28.529325012980813,
"main_function_nloc": 6,
"max_cognitive_complexity": 4,
"max_cyclomatic_complexity": 4,
"max_fan_out": 1,
"max_maximum_nesting_depth": 2,
"max_nloc": 6,
"max_parameter_count": 1,
"mean_cognitive_complexity": 2... | e539e184cd859779acc149168940c3fc44bb703abd646dcab6b8abefdf43e62e |
counting_and_aggregation_hard_003 | counting_and_aggregation | medium | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | from collections import Counter
def find_common_maxima(lists: list[list[int]]) -> list[int]:
maxima = [max(sub) for sub in lists if sub]
counts = Counter(maxima)
result = [val for val, cnt in counts.items() if cnt >= 2]
return sorted(result) | 15 | 15 | true | 80 | 0.2 | 1 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 0,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 4.754887502163469,
"main_function_nloc": 5,
"max_cognitive_complexity": 6,
"max_cyclomatic_complexity": 5,
"max_fan_out": 0,
"max_maximum_nesting_depth": 2,
"max_nloc": 5,
"max_parameter_count": 1,
"mean_cognitive_complexity": 6... | 1629614b49040a8dd91dee54448ccc09bbacb9b97adc33b93341d6e84a1d1c84 |
counting_and_aggregation_hard_004 | counting_and_aggregation | hard | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | from collections import Counter
def _single_odd(sub: list[int]) -> int | None:
odds = [x for x in sub if x % 2 != 0]
if len(odds) == 1:
return odds[0]
return None
def unique_single_odds_in_multiple_sublists(lists: list[list[int]]) -> list[int]:
singles = [_single_odd(sub) for sub in lists if s... | 15 | 15 | true | 60 | 0.6 | 1 | 1 | 0 | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 0,
"main_function_nloc": 1,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 0,
"max_maximum_nesting_depth": 1,
"max_nloc": 1,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 1,
"coupling_density": 0.5,
"halstead_volume": 58.60335893412778,
"main_function_nloc": 5,
"max_cognitive_complexity": 9,
"max_cyclomatic_complexity": 7,
"max_fan_out": 1,
"max_maximum_nesting_depth": 2,
"max_nloc": 5,
"max_parameter_count": 1,
"mean_cognitive_complexity": 6.... | 79ff697744bf51b7d56d8db71c3d2f8a834e39a66ec4dfab332e3b1357f2a1c3 |
counting_and_aggregation_hard_005 | counting_and_aggregation | medium | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | from collections import Counter
def find_common_minimums(lists: list[list[int]]) -> list[int]:
counts = Counter()
for sub in lists:
if sub:
counts[min(sub)] += 1
return sorted(k for k, v in counts.items() if v >= 3) | 15 | 15 | true | 75 | 0 | 1 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 0,
"main_function_nloc": 0,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 15.509775004326936,
"main_function_nloc": 6,
"max_cognitive_complexity": 6,
"max_cyclomatic_complexity": 5,
"max_fan_out": 0,
"max_maximum_nesting_depth": 2,
"max_nloc": 6,
"max_parameter_count": 1,
"mean_cognitive_complexity": ... | b9fac0d50185cfda944be6ccfedcc54d02a5b94e2a0c1090889588b5f24e83bb |
counting_and_aggregation_hard_006 | counting_and_aggregation | hard | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | from collections import Counter
def indices_with_common_values(lists: list[list[int]]) -> list[int]:
result = set()
max_len = max((len(sub) for sub in lists), default=0)
for idx in range(max_len):
values = [sub[idx] for sub in lists if idx < len(sub)]
counts = Counter(set(values) & set(val... | 15 | 15 | true | 57.5 | 0 | 0.5 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 0,
"main_function_nloc": 0,
"max_cognitive_complexity": 0,
"max_cyclomatic_complexity": 0,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 0,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 28.529325012980813,
"main_function_nloc": 11,
"max_cognitive_complexity": 12,
"max_cyclomatic_complexity": 7,
"max_fan_out": 0,
"max_maximum_nesting_depth": 3,
"max_nloc": 11,
"max_parameter_count": 1,
"mean_cognitive_complexity... | 8de746c5070a727b62d7a46eea89521e9b0dc9c1bbf6bb24451793f2734d56a5 |
counting_and_aggregation_hard_007 | counting_and_aggregation | medium | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | from math import gcd
from functools import reduce
from collections import Counter
def _sublist_gcd(sub: list[int]) -> int:
return reduce(gcd, sub)
def gcds_in_multiple_sublists(data: list[list[int]]) -> list[int]:
gcds = [_sublist_gcd(sub) for sub in data if sub]
counts = Counter(gcds)
result = [val... | 15 | 15 | true | 65 | 0.8 | 1 | 1 | 0 | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 1,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 0,
"max_maximum_nesting_depth": 1,
"max_nloc": 1,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 1,
"coupling_density": 0.5,
"halstead_volume": 4.754887502163469,
"main_function_nloc": 5,
"max_cognitive_complexity": 6,
"max_cyclomatic_complexity": 5,
"max_fan_out": 1,
"max_maximum_nesting_depth": 2,
"max_nloc": 5,
"max_parameter_count": 1,
"mean_cognitive_complexity": 3,... | 2eef09f693a7a75d044b844d9acb14575e20bd8c3ae616e2801a97e109c75622 |
counting_and_aggregation_hard_008 | counting_and_aggregation | hard | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | from collections import Counter
def get_sublist_modes(sublist: list[int]) -> set[int]:
counts = Counter(sublist)
max_count = max(counts.values())
return {value for value, cnt in counts.items() if cnt == max_count}
def find_modes_in_sublists(sublists: list[list[int]]) -> list[int]:
appearance_counts ... | 15 | 15 | true | 50 | 0.2 | 1 | 1 | 0 | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 0,
"main_function_nloc": 0,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 0,
"max_maximum_nesting_depth": 1,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 1,
"coupling_density": 0.5,
"halstead_volume": 38.053747805010275,
"main_function_nloc": 10,
"max_cognitive_complexity": 8,
"max_cyclomatic_complexity": 6,
"max_fan_out": 1,
"max_maximum_nesting_depth": 2,
"max_nloc": 10,
"max_parameter_count": 1,
"mean_cognitive_complexity":... | 0cade47302e4a1bffce9a98b2db323b5f7ff0c47ed1d2b360d386fd97b91d4a0 |
counting_and_aggregation_hard_009 | counting_and_aggregation | medium | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | from collections import Counter
def unique_minimums_in_three_sublists(lists: list[list[int]]) -> list[int]:
counts = Counter()
for sub in lists:
if not sub:
continue
m = min(sub)
if sub.count(m) == 1:
counts[m] += 1
return sorted(v for v, c in counts.items() ... | 15 | 15 | true | 95 | 0.8 | 1 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 1,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 1,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 36.541209043760986,
"main_function_nloc": 9,
"max_cognitive_complexity": 8,
"max_cyclomatic_complexity": 6,
"max_fan_out": 0,
"max_maximum_nesting_depth": 2,
"max_nloc": 9,
"max_parameter_count": 1,
"mean_cognitive_complexity": ... | aeb663b4c4b1b9f1f923c4560ba3a2668c9fb11b1046028c421510c96a2268b8 |
counting_and_aggregation_hard_010 | counting_and_aggregation | medium | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | from collections import Counter
def unique_maximums_at_least_three(lists: list[list[int]]) -> list[int]:
counts = Counter()
for sub in lists:
if not sub:
continue
max_val = max(sub)
if sub.count(max_val) == 1:
counts[max_val] += 1
return sorted(val for val, c... | 15 | 15 | true | 95 | 0.8 | 1 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 1,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 1,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 36.541209043760986,
"main_function_nloc": 9,
"max_cognitive_complexity": 8,
"max_cyclomatic_complexity": 6,
"max_fan_out": 0,
"max_maximum_nesting_depth": 2,
"max_nloc": 9,
"max_parameter_count": 1,
"mean_cognitive_complexity": ... | 75ca7a9d563cecf94d2eeab62ac81956f108fbb75a21b187801441f7a32b3586 |
counting_and_aggregation_medium_004 | counting_and_aggregation | hard | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | from collections import Counter
def _char_counts(strings: list[str]) -> Counter:
counts = Counter()
for s in strings:
counts.update(set(s))
return counts
def common_characters(strings: list[str]) -> list[str]:
counts = _char_counts(strings)
result = [ch for ch, cnt in counts.items() if c... | 15 | 15 | true | 50 | 0.2 | 1 | 1 | 0 | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 0,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 0,
"max_maximum_nesting_depth": 1,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 1,
"coupling_density": 0.5,
"halstead_volume": 4.754887502163469,
"main_function_nloc": 5,
"max_cognitive_complexity": 3,
"max_cyclomatic_complexity": 3,
"max_fan_out": 1,
"max_maximum_nesting_depth": 2,
"max_nloc": 5,
"max_parameter_count": 1,
"mean_cognitive_complexity": 2,... | d5970a74bece27ef9759f4a15b119c3e98f6d123ba971e709c882d01eb3abbfa |
counting_and_aggregation_medium_005 | counting_and_aggregation | hard | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | import math
from collections import Counter
def common_in_half_lists(nested: list[list[int]]) -> list[int]:
threshold = math.ceil(len(nested) / 2) if nested else 0
counts = Counter()
for lst in nested:
counts.update(set(lst))
return sorted(k for k, v in counts.items() if v >= threshold) | 15 | 15 | true | 80 | 0.2 | 1 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 0,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 15.509775004326936,
"main_function_nloc": 6,
"max_cognitive_complexity": 5,
"max_cyclomatic_complexity": 5,
"max_fan_out": 0,
"max_maximum_nesting_depth": 2,
"max_nloc": 6,
"max_parameter_count": 1,
"mean_cognitive_complexity": ... | a5b7eb047712f1c4b8a6fe690dacf2b701691a71c7b715efb4c415ea167c1d88 |
counting_and_aggregation_medium_006 | counting_and_aggregation | medium | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | from collections import Counter
def _second_largest(sublist: list[int]) -> int | None:
unique_vals = sorted(set(sublist), reverse=True)
if len(unique_vals) < 2:
return None
return unique_vals[1]
def second_largest_in_multiple_sublists(lists: list[list[int]]) -> list[int]:
seconds = (_second_... | 15 | 15 | true | 56.25 | 0.8 | 0.75 | 1 | 0 | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 1,
"max_cognitive_complexity": 0,
"max_cyclomatic_complexity": 0,
"max_fan_out": 0,
"max_maximum_nesting_depth": 1,
"max_nloc": 1,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 1,
"coupling_density": 0.5,
"halstead_volume": 28.529325012980813,
"main_function_nloc": 6,
"max_cognitive_complexity": 7,
"max_cyclomatic_complexity": 6,
"max_fan_out": 1,
"max_maximum_nesting_depth": 2,
"max_nloc": 6,
"max_parameter_count": 1,
"mean_cognitive_complexity": 4... | 7707afd4d2722baff3fc4c2ba9f524f1c986ff26ae23176d7c3bada2366078da |
counting_and_aggregation_medium_007 | counting_and_aggregation | medium | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | from collections import Counter
def _median(lst: list[int]) -> int:
s = sorted(lst)
n = len(s)
return s[(n - 1) // 2]
def medians_in_multiple_sublists(lists: list[list[int]]) -> list[int]:
medians = [_median(sub) for sub in lists if sub]
counts = Counter(medians)
result = [val for val, cnt in ... | 15 | 15 | true | 60 | 0.6 | 1 | 1 | 0 | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 0,
"main_function_nloc": 1,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 0,
"max_maximum_nesting_depth": 1,
"max_nloc": 1,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 1,
"coupling_density": 0.5,
"halstead_volume": 28.529325012980813,
"main_function_nloc": 5,
"max_cognitive_complexity": 6,
"max_cyclomatic_complexity": 5,
"max_fan_out": 1,
"max_maximum_nesting_depth": 2,
"max_nloc": 5,
"max_parameter_count": 1,
"mean_cognitive_complexity": 3... | cea49aa376e5f5c1f47de95a44fbb523e11f9292b5e6b199cdc7fbae46142f84 |
counting_and_aggregation_medium_009 | counting_and_aggregation | hard | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | from collections import Counter
def get_modes(sublist):
counts = Counter(sublist)
max_freq = max(counts.values())
return {val for val, freq in counts.items() if freq == max_freq}
def most_frequent_in_multiple_sublists(data: list[list[int]]) -> list[int]:
appearance_count = Counter()
for sub in dat... | 15 | 15 | true | 50 | 0.2 | 1 | 1 | 0 | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 0,
"main_function_nloc": 0,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 0,
"max_maximum_nesting_depth": 1,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 1,
"coupling_density": 0.5,
"halstead_volume": 38.053747805010275,
"main_function_nloc": 10,
"max_cognitive_complexity": 8,
"max_cyclomatic_complexity": 6,
"max_fan_out": 1,
"max_maximum_nesting_depth": 2,
"max_nloc": 10,
"max_parameter_count": 1,
"mean_cognitive_complexity":... | 74e99e3fcc3a6aa5c3b28f09f2d4cd916faa2697a763ca9f797c514ab9683c45 |
counting_and_aggregation_medium_010 | counting_and_aggregation | hard | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | def common_index_value(lists: list[list[int]]) -> list[int]:
non_empty = [l for l in lists if l]
if not non_empty:
return []
min_len = min(len(l) for l in non_empty)
result = [non_empty[0][i] for i in range(min_len) if all(l[i] == non_empty[0][i] for l in non_empty)]
return sorted(set(result... | 15 | 15 | true | 77.5 | 0.8 | 0.5 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 0,
"main_function_nloc": 1,
"max_cognitive_complexity": 0,
"max_cyclomatic_complexity": 0,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 1,
"max_parameter_count": 1,
"mean_cognitive_complexity": 0,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 11.60964047443681,
"main_function_nloc": 7,
"max_cognitive_complexity": 10,
"max_cyclomatic_complexity": 8,
"max_fan_out": 0,
"max_maximum_nesting_depth": 2,
"max_nloc": 7,
"max_parameter_count": 1,
"mean_cognitive_complexity": ... | 5207f54dd985d7bdc2a0b93f43de9434a3ba3a82334443dbd6161aabb5fdb2aa |
date_and_time_easy_001 | date_and_time | easy | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | from datetime import datetime
def get_weekday(date_str: str) -> str:
dt = datetime.strptime(date_str, "%Y-%m-%d")
days = ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"]
return days[dt.weekday()] | 15 | 15 | true | 80 | 0.2 | 1 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 0,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 0,
"main_function_nloc": 4,
"max_cognitive_complexity": 0,
"max_cyclomatic_complexity": 1,
"max_fan_out": 0,
"max_maximum_nesting_depth": 0,
"max_nloc": 4,
"max_parameter_count": 1,
"mean_cognitive_complexity": 0,
"mean_cyclom... | f3cd47386d4d3443db4143608ff84f5ec0f9acb719179f5fd197b100096c535f |
date_and_time_easy_002 | date_and_time | easy | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | from datetime import date
def days_between_dates(date1: str, date2: str) -> int:
d1 = date.fromisoformat(date1)
d2 = date.fromisoformat(date2)
return (d2 - d1).days | 15 | 15 | true | 80 | 0.2 | 1 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 0,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 4.754887502163469,
"main_function_nloc": 4,
"max_cognitive_complexity": 0,
"max_cyclomatic_complexity": 1,
"max_fan_out": 0,
"max_maximum_nesting_depth": 0,
"max_nloc": 4,
"max_parameter_count": 2,
"mean_cognitive_complexity": 0... | 5f4633e81e58015c5f5573ee096bb53148f3602f36df07a1a54a247c04fa52ba |
date_and_time_easy_003 | date_and_time | easy | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | def seconds_to_time_string(seconds: int) -> str:
h = seconds // 3600
m = (seconds % 3600) // 60
s = seconds % 60
return f"{h:02d}:{m:02d}:{s:02d}" | 15 | 15 | true | 75 | 0 | 1 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 0,
"main_function_nloc": 0,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 31.019550008653873,
"main_function_nloc": 5,
"max_cognitive_complexity": 0,
"max_cyclomatic_complexity": 1,
"max_fan_out": 0,
"max_maximum_nesting_depth": 0,
"max_nloc": 5,
"max_parameter_count": 1,
"mean_cognitive_complexity": ... | 0e7a4fddb2829fccbe87c5334bd8bfc8121ee9c439e38970b0bdd2a437bf87fb |
date_and_time_easy_004 | date_and_time | easy | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | from datetime import datetime
def is_weekend(date_str: str) -> bool:
d = datetime.strptime(date_str, "%Y-%m-%d")
return d.weekday() >= 5 | 15 | 15 | true | 80 | 0.2 | 1 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 0,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 4.754887502163469,
"main_function_nloc": 3,
"max_cognitive_complexity": 0,
"max_cyclomatic_complexity": 1,
"max_fan_out": 0,
"max_maximum_nesting_depth": 0,
"max_nloc": 3,
"max_parameter_count": 1,
"mean_cognitive_complexity": 0... | f16d8f15e4befb51a712d36b36b181e134112c6b1a13027791a377bc698f7b95 |
date_and_time_easy_006 | date_and_time | medium | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | import re
def is_on_the_hour(time_str: str) -> bool:
match = re.fullmatch(r'([01]\d|2[0-3]):([0-5]\d)', time_str)
if not match:
return False
return match.group(2) == '00' | 15 | 15 | true | 80 | 0.2 | 1 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 0,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 11.60964047443681,
"main_function_nloc": 5,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 2,
"max_fan_out": 0,
"max_maximum_nesting_depth": 1,
"max_nloc": 5,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1... | c5e792ab03d526f3ae9d6678a9c42afcc7087b744a9de6808abc604849fb15c3 |
date_and_time_easy_007 | date_and_time | hard | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | import calendar
def days_in_month(date_str: str) -> int:
year, month, _ = date_str.split("-")
return calendar.monthrange(int(year), int(month))[1] | 15 | 15 | true | 100 | 1 | 1 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 1,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 1,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 0,
"main_function_nloc": 3,
"max_cognitive_complexity": 0,
"max_cyclomatic_complexity": 1,
"max_fan_out": 0,
"max_maximum_nesting_depth": 0,
"max_nloc": 3,
"max_parameter_count": 1,
"mean_cognitive_complexity": 0,
"mean_cyclom... | 8969510e41b7cc616eeda9bd9c89e3620d59265d317d86e1d60aa0d451122de6 |
date_and_time_easy_008 | date_and_time | easy | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | def minutes_since_midnight(time_str: str) -> int:
h, m = time_str.split(":")
return int(h) * 60 + int(m) | 15 | 15 | true | 100 | 1 | 1 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 1,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 1,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 15.509775004326936,
"main_function_nloc": 3,
"max_cognitive_complexity": 0,
"max_cyclomatic_complexity": 1,
"max_fan_out": 0,
"max_maximum_nesting_depth": 0,
"max_nloc": 3,
"max_parameter_count": 1,
"mean_cognitive_complexity": ... | afea1764a1dd340c5c4005d142154394616f195e8072e8da1b3c99e61b47718c |
date_and_time_easy_009 | date_and_time | easy | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | def is_first_day_of_month(date_str: str) -> bool:
day = date_str.split('-')[2]
return day == '01' | 15 | 15 | true | 80 | 0.2 | 1 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 0,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 4.754887502163469,
"main_function_nloc": 3,
"max_cognitive_complexity": 0,
"max_cyclomatic_complexity": 1,
"max_fan_out": 0,
"max_maximum_nesting_depth": 0,
"max_nloc": 3,
"max_parameter_count": 1,
"mean_cognitive_complexity": 0... | 19132177c80fd5621e627f5f70388bb4367c99cdf86a8091631a9a0eec7e988f |
date_and_time_hard_001 | date_and_time | medium | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | from datetime import date, timedelta
def count_unique_days(intervals: list[list[str]]) -> int:
days = set()
for start_str, end_str in intervals:
start = date.fromisoformat(start_str)
end = date.fromisoformat(end_str)
if start > end:
start, end = end, start
current = ... | 15 | 15 | true | 51.25 | 0.4 | 0.25 | 1 | 0.75 | {
"call_graph_depth": 1,
"coupling_density": 0,
"halstead_volume": 1,
"main_function_nloc": 0,
"max_cognitive_complexity": 0,
"max_cyclomatic_complexity": 0,
"max_fan_out": 1,
"max_maximum_nesting_depth": 0,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 0,
"mean_cyclomati... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 25.26619429851844,
"main_function_nloc": 12,
"max_cognitive_complexity": 5,
"max_cyclomatic_complexity": 4,
"max_fan_out": 0,
"max_maximum_nesting_depth": 2,
"max_nloc": 12,
"max_parameter_count": 1,
"mean_cognitive_complexity":... | f3539de4be334773f3f4112a81af7e45c87c4c4e6f1c5bdd62980e47ab536e8b |
date_and_time_hard_003 | date_and_time | hard | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | from datetime import datetime
def _to_seconds(dt_str: str) -> int:
dt = datetime.strptime(dt_str, "%Y-%m-%d %H:%M:%S")
return int(dt.timestamp())
def _normalize(intervals: list[list[str]]) -> list[tuple[int, int]]:
normalized = []
for start_str, end_str in intervals:
a = _to_seconds(start_str)... | 15 | 15 | true | 62.5 | 0.2 | 0.5 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": 1,
"halstead_volume": 1,
"main_function_nloc": 0,
"max_cognitive_complexity": 0,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 0,
"mean_cyclomati... | {
"call_graph_depth": 2,
"coupling_density": 0.3333333333333333,
"halstead_volume": 101.57915548582149,
"main_function_nloc": 15,
"max_cognitive_complexity": 5,
"max_cyclomatic_complexity": 4,
"max_fan_out": 1,
"max_maximum_nesting_depth": 2,
"max_nloc": 15,
"max_parameter_count": 1,
"mean_cogniti... | 30ca94e86c8250f302146acf8c0501b816689211951dd58f07a6c07a8a9ec9e0 |
date_and_time_hard_004 | date_and_time | hard | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | import datetime as _dt
def _to_dates(intervals: list[list[str]]) -> list[tuple[_dt.date, _dt.date]]:
pairs = []
for a, b in intervals:
da = _dt.date.fromisoformat(a)
db = _dt.date.fromisoformat(b)
start = min(da, db)
end = max(da, db)
pairs.append((start, end))
pair... | 15 | 15 | true | 55 | 0.4 | 1 | 1 | 0 | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 0,
"main_function_nloc": 1,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 0,
"max_maximum_nesting_depth": 1,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 1,
"coupling_density": 0.3333333333333333,
"halstead_volume": 48.43204266092217,
"main_function_nloc": 5,
"max_cognitive_complexity": 4,
"max_cyclomatic_complexity": 4,
"max_fan_out": 2,
"max_maximum_nesting_depth": 2,
"max_nloc": 11,
"max_parameter_count": 1,
"mean_cognitive... | 6f28ea2fcfc51f1a9f54f30a41d40700da846fdcfd5ff054f826e75a798b8b39 |
date_and_time_hard_005 | date_and_time | hard | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | from datetime import datetime, timedelta
def _parse(s: str) -> datetime:
return datetime.strptime(s, "%Y-%m-%d %H:%M:%S")
def _fmt(d: datetime) -> str:
return d.strftime("%Y-%m-%d %H:%M:%S")
def _normalize(intervals: list[list[str]]) -> list[tuple[datetime, datetime]]:
result = []
for start_str, end_... | 15 | 15 | true | 50.625 | 0.4 | 0.875 | 1 | 0 | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 0,
"main_function_nloc": 1,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 0,
"max_maximum_nesting_depth": 0,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 2,
"coupling_density": 0.2,
"halstead_volume": 104.2481250360578,
"main_function_nloc": 7,
"max_cognitive_complexity": 8,
"max_cyclomatic_complexity": 5,
"max_fan_out": 3,
"max_maximum_nesting_depth": 3,
"max_nloc": 10,
"max_parameter_count": 1,
"mean_cognitive_complexity": 2... | 62ebe0364fee73c52355288ccc7e0f47f7d9afa42a6e508172b6f7b2a39caeee |
date_and_time_hard_006 | date_and_time | hard | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | from datetime import date, timedelta
from collections import Counter
def _parse_date(s: str) -> date:
y, m, d = map(int, s.split('-'))
return date(y, m, d)
def _date_range(d1: date, d2: date) -> list[date]:
days = (d2 - d1).days
return [d1 + timedelta(days=i) for i in range(days + 1)]
def dates_wi... | 15 | 15 | true | 71.25 | 0.8 | 0.75 | 1 | 0.5 | {
"call_graph_depth": 1,
"coupling_density": 1,
"halstead_volume": 1,
"main_function_nloc": 0,
"max_cognitive_complexity": 0,
"max_cyclomatic_complexity": 0,
"max_fan_out": 0,
"max_maximum_nesting_depth": 1,
"max_nloc": 1,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclomati... | {
"call_graph_depth": 1,
"coupling_density": 0.3333333333333333,
"halstead_volume": 70.32403072095333,
"main_function_nloc": 10,
"max_cognitive_complexity": 9,
"max_cyclomatic_complexity": 7,
"max_fan_out": 2,
"max_maximum_nesting_depth": 2,
"max_nloc": 10,
"max_parameter_count": 2,
"mean_cognitiv... | c09b333d7e9dbfc813fb1c75a05c2a9aad719c8f549d5a26ef8ca761d6f0c690 |
date_and_time_hard_007 | date_and_time | medium | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | from datetime import datetime
def _is_friday_13th(date_str: str) -> bool:
d = datetime.strptime(date_str, "%Y-%m-%d")
return d.day == 13 and d.weekday() == 4
def earliest_friday_13th(dates: list[str]) -> str | None:
candidates = [d for d in dates if _is_friday_13th(d)]
if not candidates:
ret... | 15 | 15 | true | 65 | 0.8 | 1 | 1 | 0 | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 1,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 0,
"max_maximum_nesting_depth": 1,
"max_nloc": 1,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 1,
"coupling_density": 0.5,
"halstead_volume": 36.541209043760986,
"main_function_nloc": 5,
"max_cognitive_complexity": 4,
"max_cyclomatic_complexity": 4,
"max_fan_out": 1,
"max_maximum_nesting_depth": 2,
"max_nloc": 5,
"max_parameter_count": 1,
"mean_cognitive_complexity": 2... | d1f0c44be25d3f8e7b97ed610b23577996dbe55b6597543ebe6328c172180f02 |
date_and_time_hard_008 | date_and_time | hard | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | from datetime import date, timedelta
def _parse(s: str) -> date:
y, m, d = map(int, s.split("-"))
return date(y, m, d)
def _merge(intervals: list[list[str]]) -> list[tuple[date, date]]:
pairs = []
for a, b in intervals:
da, db = _parse(a), _parse(b)
pairs.append((min(da, db), max(da, d... | 15 | 15 | true | 53.125 | 0.6 | 0.375 | 1 | 0.5 | {
"call_graph_depth": 0,
"coupling_density": 1,
"halstead_volume": 0,
"main_function_nloc": 1,
"max_cognitive_complexity": 0,
"max_cyclomatic_complexity": 0,
"max_fan_out": 1,
"max_maximum_nesting_depth": 0,
"max_nloc": 1,
"max_parameter_count": 1,
"mean_cognitive_complexity": 0,
"mean_cyclomati... | {
"call_graph_depth": 2,
"coupling_density": 0.3333333333333333,
"halstead_volume": 140.2304206377674,
"main_function_nloc": 9,
"max_cognitive_complexity": 9,
"max_cyclomatic_complexity": 6,
"max_fan_out": 1,
"max_maximum_nesting_depth": 3,
"max_nloc": 14,
"max_parameter_count": 1,
"mean_cognitive... | 23626b1a14ae9fd07d8b4cf236dbb30e43afbdfcd73a23fc9500721edb3651ea |
date_and_time_hard_009 | date_and_time | hard | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | import datetime
import collections
FMT = "%Y-%m-%d %H:%M:%S"
def _compute(intervals, k):
events = collections.defaultdict(int)
for a, b in intervals:
da = datetime.datetime.strptime(a, FMT)
db = datetime.datetime.strptime(b, FMT)
lo, hi = min(da, db), max(da, db)
events[lo] +=... | 15 | 15 | true | 64.375 | 0.6 | 0.625 | 0.5 | 0.75 | {
"call_graph_depth": 1,
"coupling_density": 0,
"halstead_volume": 1,
"main_function_nloc": 1,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 0,
"mean_cyclomati... | {
"call_graph_depth": 1,
"coupling_density": 0.5,
"halstead_volume": 123.18989788986397,
"main_function_nloc": 4,
"max_cognitive_complexity": 7,
"max_cyclomatic_complexity": 5,
"max_fan_out": 1,
"max_maximum_nesting_depth": 3,
"max_nloc": 20,
"max_parameter_count": 2,
"mean_cognitive_complexity": ... | 95c6f19badbdf2c4f3b0441415588435bcfa408d60d459a4e61bedc51221c411 |
date_and_time_medium_001 | date_and_time | easy | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | from datetime import datetime
def seconds_between_datetimes(dt1: str, dt2: str) -> int:
fmt = '%Y-%m-%d %H:%M:%S'
d1 = datetime.strptime(dt1, fmt)
d2 = datetime.strptime(dt2, fmt)
return int((d2 - d1).total_seconds()) | 15 | 15 | true | 100 | 1 | 1 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 1,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 1,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 4.754887502163469,
"main_function_nloc": 5,
"max_cognitive_complexity": 0,
"max_cyclomatic_complexity": 1,
"max_fan_out": 0,
"max_maximum_nesting_depth": 0,
"max_nloc": 5,
"max_parameter_count": 2,
"mean_cognitive_complexity": 0... | 9dcb299b5e3975e89c596aaccd8a7dc173ae3283abce31f522f2d3340b61e175 |
date_and_time_medium_002 | date_and_time | hard | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | from datetime import datetime, timedelta
def previous_day(date_str: str) -> str:
dt = datetime.strptime(date_str, "%Y-%m-%d")
prev = dt - timedelta(days=1)
return prev.strftime("%Y-%m-%d") | 15 | 15 | true | 100 | 1 | 1 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 1,
"main_function_nloc": 1,
"max_cognitive_complexity": 1,
"max_cyclomatic_complexity": 1,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 1,
"max_parameter_count": 1,
"mean_cognitive_complexity": 1,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 4.754887502163469,
"main_function_nloc": 4,
"max_cognitive_complexity": 0,
"max_cyclomatic_complexity": 1,
"max_fan_out": 0,
"max_maximum_nesting_depth": 0,
"max_nloc": 4,
"max_parameter_count": 1,
"mean_cognitive_complexity": 0... | a2d40039b7594d61c52504a0d8677fa2a808d1d3f9cc87d7a727d392c994abab |
date_and_time_medium_004 | date_and_time | easy | EP and BVA | anthropic/claude-sonnet-5 | large | baseline | from datetime import datetime, timedelta
def dates_in_range(start_date: str, end_date: str) -> list[str]:
start = datetime.strptime(start_date, '%Y-%m-%d')
end = datetime.strptime(end_date, '%Y-%m-%d')
if end < start:
return []
days = (end - start).days
return [(start + timedelta(days=i)).s... | 15 | 15 | true | 57.5 | 0 | 0.5 | 1 | 1 | {
"call_graph_depth": 1,
"coupling_density": null,
"halstead_volume": 0,
"main_function_nloc": 0,
"max_cognitive_complexity": 0,
"max_cyclomatic_complexity": 0,
"max_fan_out": 1,
"max_maximum_nesting_depth": 1,
"max_nloc": 0,
"max_parameter_count": 1,
"mean_cognitive_complexity": 0,
"mean_cyclom... | {
"call_graph_depth": 0,
"coupling_density": null,
"halstead_volume": 36,
"main_function_nloc": 7,
"max_cognitive_complexity": 2,
"max_cyclomatic_complexity": 3,
"max_fan_out": 0,
"max_maximum_nesting_depth": 1,
"max_nloc": 7,
"max_parameter_count": 2,
"mean_cognitive_complexity": 2,
"mean_cyclo... | 548984416bd45858d7eb0cdd53e3ad14c916795de715c41aa15e23ca44ee4db6 |
End of preview. Expand in Data Studio
Code Complexity Model Predictions
data/predictions.jsonl contains 7,160 saved Python code outputs: ten model
variants, 358 selected tasks, and baseline and guided conditions. The viewer
table is keyed by task_id, model, and condition. It includes the
test_methods value from the companion benchmark, the revised 15-test
result, 19 metric decisions, four group scores, and the joint score.
The joint score is zero unless all 15 tests pass. For passing outputs, metric decisions are averaged within Size, Control flow, Interface, and Coupling, then combined at 25%, 35%, 10%, and 30%. The joint score is on a 0 to 100 scale; group scores are on a 0 to 1 scale. A null metric decision means the metric was unavailable and was omitted from its group average.
The model answers were saved before the final 358-task selection and hidden test revision. These results reuse the saved answers and retest them against the revised suites. They are a retrospective replay, not new generations. The companion benchmark dataset contains the task specifications, tests, and tested references.
The audit/final_358_* files record the revised benchmark audit, score
manifest, and summary tables. Earlier raw_requests/, raw_responses/,
source_results/, and other audit/ files preserve the 368-task run that
supplied the answers. The saved code hashes were checked before this update.
These tests sample specified behavior. Structural metrics indicate code form and are not direct human ratings of understandability.
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