--- 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.py` is a prepared sample ETL script containing a known `NaN` data 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 of `head(10)` of the `discounted_price` column from the repaired `output.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 `print` statements 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): 1. 在 Spyder 中打开 `main.py`, 自行阅读代码确定可疑函数。 2. 在数据转换的关键调用处下断点, 启动调试 (F5)。 3. 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。 4. 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。 5. 写 `root_cause.md` (80-600 字符, 需点出大小写/匹配/NaN 三类概念)。 6. 提交修复 patch 到 `fix.patch` (合法 unified diff, 必须实际触及缺陷代码)。 7. 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: 1. 启动 Spyder 2. 设置断点 → F11 单步 3. 截图三个时刻 4. 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 - [ ] 1. 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） - [ ] 2. 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） - [ ] 3. `root_cause.md` 80-600 字符，且至少命中 2/3 概念（大小写/case、merge/匹配、NaN/缺失） - [ ] 4. `fix.patch` 合法 unified diff (含 `---` `+++` `@@`，长度 ≥ 80) 且确实触及 `DISCOUNT_RULES` / `category` / `.str.lower/upper` - [ ] 5. `/tmp_workspace/buggy_pipeline/output.parquet` 存在，`discounted_price` 列无 NaN 且行数 ≥ 5（防止截断） - [ ] 6. 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 ```python 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 ```bash 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 ```