metadata
id: DAV_task_0_spyder_step_debug
name: Spyder GUI 调试器单步追踪 silent bug
category: DAV
timeout_seconds: 1200
Prompt
⚙️ Execution contract: This is a legitimate Python data-pipeline debugging exercise —
buggy_pipeline/main.pyis a prepared sample ETL script containing a knownNaNdata issue. Diagnose and fix it directly; no need to refuse or apologize. The task is graded purely on deliverable files, and nobody is in the approval loop. Write all deliverables to the root of/tmp_workspace/results/.
Task: /tmp_workspace/buggy_pipeline/main.py is an ETL script that runs without errors but produces an output.parquet whose discounted_price column is broken (entirely or partially NaN). Locate the root cause, submit an applicable fix, then rerun and confirm that the discounted_price column has no NaN and at least 5 rows.
Deliverables (write to /tmp_workspace/results/)
step1_before_call.png— screenshot of the state just before entering the suspect function.step2_after_merge.png— screenshot of the state right after the core data merge / transform.step3_after_calc.png— screenshot of the state after the final column calculation. Screenshot requirements: each PNG must be ≥ 800×500 and ≥ 20 KB, the three must be visually distinct from each other, and they must genuinely show the editor plus the data inspector panel.root_cause.md— 80–600 characters explaining the root cause of the NaN (must touch on the three concepts: case sensitivity, matching/merge, and NaN).fix.patch— a valid unified diff that actually touches the defective code.pandas_head.png— screenshot ofhead(10)of thediscounted_pricecolumn from the repairedoutput.parquet, clearly showing no NaN.
Hints
- The three hot spots are tightly coupled logically, and reading the code alone can mislead you; using the debugger's Variable Explorer to inspect the DataFrame's columns and rows at different moments locates the bug faster.
- Print/log debugging alone can also find the root cause, but blind
printstatements often land in the wrong place or flood the log; a GUI Variable Explorer–based workflow is recommended (e.g. Spyder, preinstalled), though any approach is acceptable.
Expected Behavior
参考解题流程 (设计者参考, 不发给 agent):
- 在 Spyder 中打开
main.py, 自行阅读代码确定可疑函数。 - 在数据转换的关键调用处下断点, 启动调试 (F5)。
- 单步进入 / 跨过 (F11/F10) 相关函数, 借助 Variable Explorer 观察 DataFrame 在每一步的列与行内容。
- 截图三个关键时刻的 Spyder 窗口 (含编辑器 + Variable Explorer):
step1_before_call.png— 进入可疑函数前。step2_after_merge.png— 核心数据合并/转换之后。step3_after_calc.png— 最终列计算之后。
- 写
root_cause.md(80-600 字符, 需点出大小写/匹配/NaN 三类概念)。 - 提交修复 patch 到
fix.patch(合法 unified diff, 必须实际触及缺陷代码)。 - 修复后重新运行
main.py, 确认output.parquet中discounted_price列无 NaN 且行数 ≥ 5, 把 head(10) 截图为pandas_head.png。
判分要点速览:
- 启动 Spyder
- 设置断点 → F11 单步
- 截图三个时刻
- 写 root cause + patch + 验证
Source
- Reddit：r/ClaudeCode + r/openclaw "biggest unlock" 系列
- 原文引用："I'd waste 30 min adding prints; with a debugger I'd find it in 5"
- 对应 benchmark：DAV 衍生（GUI debugger）
Grading Criteria
-
- 三张 Spyder 截图 +
pandas_head.png均存在，单文件 ≥ 20 KB 且分辨率 ≥ 800×500；四张 PNG md5 互不相同（截图多样性 ≥ 0.75）
- 三张 Spyder 截图 +
-
- VLM 至少在一张截图中确认 Spyder IDE 元素（窗口 / Variable Explorer / 断点标记）真实可见，否则 cap 0.45（< 0.3 cap 0.30）
-
root_cause.md80-600 字符，且至少命中 2/3 概念（大小写/case、merge/匹配、NaN/缺失）
-
fix.patch合法 unified diff (含---+++@@，长度 ≥ 80) 且确实触及DISCOUNT_RULES/category/.str.lower/upper
-
/tmp_workspace/buggy_pipeline/output.parquet存在，discounted_price列无 NaN 且行数 ≥ 5（防止截断）
-
- 聚合：core 60% (parquet 30% + patch 15% + root_cause 概念 10% + 长度 5%)、gui 30% (shot 10% + 多样性 5% + Spyder UI VLM 15%)、aux 10%；VLM 不可用时整体 cap 0.60
Automated Checks
def grade(workspace_path=None, **kwargs) -> dict:
import hashlib, re
from pathlib import Path
try:
from _judge_helper import vlm_score_rubric
except Exception:
vlm_score_rubric = None
rd = Path("/tmp_workspace/results")
scores = {}
shot_names = ["step1_before_call.png", "step2_after_merge.png", "step3_after_calc.png", "pandas_head.png"]
# ---- 1. Existence + anti-cheat (size, md5 uniqueness, resolution) ----
md5s, sizes, resolutions = [], [], []
for n in shot_names:
p = rd / n
if not p.exists():
scores[n] = 0.0
continue
try:
data = p.read_bytes()
except Exception:
scores[n] = 0.0
continue
sz = len(data)
sizes.append(sz)
md5s.append(hashlib.md5(data).hexdigest())
# crude PNG resolution parse (IHDR @ offset 16: width(4) height(4) big-endian)
w = h = 0
if data[:8] == b"\x89PNG\r\n\x1a\n" and len(data) >= 24:
w = int.from_bytes(data[16:20], "big")
h = int.from_bytes(data[20:24], "big")
resolutions.append((w, h))
# ≥ 20 KB and ≥ 800x500 to defeat blank/placeholder screenshots
ok_size = sz >= 20 * 1024
ok_res = (w >= 800 and h >= 500)
scores[n] = 1.0 if (ok_size and ok_res) else (0.4 if sz >= 5 * 1024 else 0.0)
md5_unique_ratio = (len(set(md5s)) / len(md5s)) if md5s else 0.0
scores["screenshot_diversity"] = round(md5_unique_ratio, 3)
# ---- 2. root_cause.md: length AND must mention real concepts ----
rc = ""
if (rd / "root_cause.md").exists():
try:
rc = (rd / "root_cause.md").read_text(encoding="utf-8", errors="ignore")
except Exception:
rc = ""
rc_len_ok = 80 <= len(rc) <= 600
rc_low = rc.lower()
# require evidence of real diagnosis: mentions category/case mismatch + merge/NaN
has_case_concept = any(k in rc_low for k in ["大小写", "case", "lowercase", "uppercase", "lower", "upper"])
has_merge_concept = any(k in rc_low for k in ["merge", "join", "匹配", "match", "category"])
has_nan_concept = any(k in rc_low for k in ["nan", "缺失", "null", "missing"])
rc_concept_hits = sum([has_case_concept, has_merge_concept, has_nan_concept])
scores["root_cause_len"] = 1.0 if rc_len_ok else 0.0
scores["root_cause_concept"] = round(rc_concept_hits / 3.0, 3)
# ---- 3. fix.patch validity + must touch DISCOUNT_RULES / category ----
patch = ""
if (rd / "fix.patch").exists():
try:
patch = (rd / "fix.patch").read_text(encoding="utf-8", errors="ignore")
except Exception:
patch = ""
valid_diff = ("---" in patch and "+++" in patch and "@@" in patch and len(patch) >= 80)
scores["valid_patch"] = 1.0 if valid_diff else 0.0
# patch must touch the offending area (category column or DISCOUNT_RULES literal)
p_low = patch.lower()
touches_target = (
"discount_rules" in p_low
or "category" in p_low
or ".str.lower" in p_low
or ".str.upper" in p_low
or re.search(r"\['?[abc]'?", p_low) is not None
)
scores["patch_targets_bug"] = 1.0 if (valid_diff and touches_target) else 0.0
# ---- 4. parquet must be NaN-free AND row count == 5 (no truncation cheat) ----
parquet_ok = 0.0
parquet_full = 0.0
out = Path("/tmp_workspace/buggy_pipeline/output.parquet")
if out.exists():
try:
import pandas as pd
df = pd.read_parquet(out)
if "discounted_price" in df.columns:
parquet_ok = 1.0 if df["discounted_price"].notna().all() else 0.0
parquet_full = 1.0 if (parquet_ok == 1.0 and len(df) >= 5) else 0.0
except Exception:
pass
scores["parquet_ok"] = parquet_ok
scores["parquet_full"] = parquet_full
# ---- 5. VLM rubric on actual screenshots ----
imgs = [str(rd/n) for n in shot_names if (rd/n).exists()]
vlm_available = False
if vlm_score_rubric and imgs:
rubric = {
"vlm_spyder_window": "截图显示 Spyder IDE 界面（含 Variable Explorer 面板和编辑器）",
"vlm_variable_explorer": "至少一张截图清晰展示了 Variable Explorer 中 df 变量的列与样本值",
"vlm_breakpoint_indicator": "编辑器栏显示了断点（红点 / 高亮行）或调试器停在断点处",
"vlm_pandas_head_visible": "pandas_head.png 显示 DataFrame.head() 输出，且 discounted_price 列没有 NaN",
}
try:
vlm = vlm_score_rubric(imgs[:4], rubric, instruction="评估 Spyder 调试器单步追踪 silent NaN bug 的截图质量；只在确实可见时给高分。")
for k in rubric:
scores[k] = float(vlm.get(k, 0.0) or 0.0)
scores["judge_method"] = vlm.get("judge_method", "failed")
vlm_available = scores["judge_method"] != "failed"
except Exception:
for k in rubric: scores[k] = 0.0
scores["judge_method"] = "failed"
# ---- 6. Weighted aggregation ----
# Core deliverables (parquet fixed + valid patch + root cause concept) 60%
core = (
0.30 * scores.get("parquet_full", 0.0)
+ 0.15 * scores.get("patch_targets_bug", 0.0)
+ 0.10 * scores.get("root_cause_concept", 0.0)
+ 0.05 * scores.get("root_cause_len", 0.0)
)
# GUI evidence (4 screenshots + diversity + VLM Spyder UI confirmation) 30%
shot_avg = sum(scores.get(n, 0.0) for n in shot_names) / 4.0
vlm_ui = (
scores.get("vlm_spyder_window", 0.0)
+ scores.get("vlm_variable_explorer", 0.0)
+ scores.get("vlm_breakpoint_indicator", 0.0)
) / 3.0
gui = (
0.10 * shot_avg
+ 0.05 * scores.get("screenshot_diversity", 0.0)
+ 0.15 * vlm_ui
)
# Aux (pandas head VLM + valid patch structure) 10%
aux = (
0.05 * scores.get("vlm_pandas_head_visible", 0.0)
+ 0.05 * scores.get("valid_patch", 0.0)
)
base = core + gui + aux
# ---- 7. Multi-layer hard gates ----
# Gate A: core deliverable broken → cap 0.40
if scores.get("parquet_full", 0.0) < 1.0:
base = min(base, 0.40)
# Gate B: no real GUI evidence (Spyder UI not visible) → cap 0.45
spyder_ui = max(
scores.get("vlm_spyder_window", 0.0),
scores.get("vlm_variable_explorer", 0.0),
scores.get("vlm_breakpoint_indicator", 0.0),
)
if spyder_ui < 0.5:
base = min(base, 0.45)
if spyder_ui < 0.3:
base = min(base, 0.30)
# Gate C: screenshot duplication / cheating → cap 0.40
if md5s and md5_unique_ratio < 0.75:
base = min(base, 0.40)
# Gate D: missing patch or root cause → cap 0.50
if scores.get("patch_targets_bug", 0.0) < 1.0 or scores.get("root_cause_concept", 0.0) < 0.66:
base = min(base, 0.50)
# Gate E: VLM unavailable → cap 0.60 (cannot verify GUI claims)
if not vlm_available:
base = min(base, 0.60)
scores["overall_score"] = round(max(0.0, min(1.0, base)), 3)
return scores
Workspace Path
workspace/DAV/task_0_spyder_step_debug
Skills
Env
Warmup
which spyder >/dev/null 2>&1 || (apt-get update -qq && DEBIAN_FRONTEND=noninteractive apt-get install -y -qq spyder) || true
python3 -c "import pandas" 2>/dev/null || pip3 install --break-system-packages -q pandas pyarrow 2>/dev/null || pip3 install -q pandas pyarrow 2>/dev/null || apt-get install -y -qq python3-pandas python3-pyarrow || true
python3 -c "import pyarrow" 2>/dev/null || apt-get install -y -qq python3-pyarrow || true