tasks/OPS/OPS_task_0_vscode_debugger_offbyone.md

metadata

id: OPS_task_0_vscode_debugger_offbyone
name: VS Code GUI 调试器抓 off-by-one bug
category: OPS
timeout_seconds: 1200

Prompt

Background: /tmp_workspace/pager/core.py::total_pages returns an incorrect page count on certain boundary inputs (off-by-one). pager/tests/test_pager.py already covers both the evenly-divisible and non-evenly-divisible boundary cases. A Debug Pager (edge case) configuration is pre-wired in the repo's launch.json (typical input total=100, page_size=10) and can be used directly to trigger the bug. Goal: reproduce and locate the bug → fix core.py → make all pytest cases pass, and produce reviewable process evidence (screenshots + analysis + patch).

Hard constraints on the main deliverable /tmp_workspace/pager/core.py

• total_pages(total, page_size) must return the correct page count for evenly-divisible, non-evenly-divisible, and boundary inputs alike
• After the fix, every case under pytest /tmp_workspace/pager/tests/ must pass

Supporting deliverables (under /tmp_workspace/results/)

File	Requirement
analysis.md	100–500 characters, explaining the root cause of the off-by-one; should touch on several of: floor / ceil / boundary / remainder / mod
fix.patch	Valid unified diff that modifies pager/core.py only (exactly one +++ b/...core.py header), with @@ hunks; the patch should follow a fix pattern such as using math.ceil
pytest.txt	Full output of pytest /tmp_workspace/pager/tests/, containing passed and free of failed / error

Four process screenshots (fixed file names, all under /tmp_workspace/results/)

File	Content
dbg_entry.png	Debugger just entered total_pages with arguments bound
dbg_after_calc.png	After stepping past the core calculation
dbg_return.png	Just before the function returns
dbg_after_fix.png	Debugging again after the fix, showing the corrected pages value

General specs: resolution ≥ 1024×600, file ≥ 5 KB, all four screenshots must have distinct md5s (anti-forgery); the debugger's variable / watch panel characteristic text must be visible in the images (grading uses OCR).

Expected Behavior

设计意图与典型解题路径（仅供出题人参考，不发给 agent）：

1. 推荐用 VS Code 内置 DAP 调试器走 GUI 路径（也可走 PyCharm / 其他可视化 Python 调试器，只要能截到变量面板即可）。
2. 在编辑器中打开 /tmp_workspace/pager/，在 core.py::total_pages 的函数定义处下断点。
3. 用预置的 Debug Pager (edge case) 配置启动调试（典型边界输入 total=100, page_size=10），单步执行，依次截下：进入函数 → 计算后 → return 前。
4. 在 analysis.md 解释 off-by-one：floor vs ceil、整除 / 余数 / 边界等。
5. 修改 core.py（推荐用 math.ceil(total / page_size) 一类公式），生成 unified diff 写入 fix.patch。
6. 重启调试验证修复后的 pages 值，截图为 dbg_after_fix.png。
7. 跑 pytest /tmp_workspace/pager/tests/ 并保存输出到 pytest.txt。

约束说明：

• 鼓励通过 GUI 调试器留下可 OCR 的视觉证据；纯 pdb / 纯 print 难以满足"截图含调试面板特征文本 + 4 张 md5 互异"的硬门槛。
• launch.json 已预置好，无需修改。

评分要点（hard gates）：

• total_pages 必须对全部 truth test case 返回正确页数（未全过会被压到 ≤0.45）
• fix.patch 必须是合法 unified diff 且仅触及 core.py（否则压到 ≤0.5）
• 4 张 dbg_*.png 至少 3 张存在，且每张 ≥5 KB / 分辨率 ≥1024×600（缺图压到 ≤0.35）
• 4 张截图 md5 必须互不相同（大量重复压到 ≤0.4）
• 截图需含调试器变量面板的可 OCR 文本特征（OCR 命中率 <50% 压到 ≤0.55）
• pytest.txt 含 passed 且无 failed / error
• analysis.md 100–500 字符且命中 ≥2 个相关概念关键词

Source

• Reddit：r/ClaudeCode + r/openclaw 多帖
• 原文引用："I keep adding prints in random places when a debugger would step me through in 2 minutes"
• 对应 benchmark：OPS 衍生（VS Code GUI debugger）

Grading Criteria

• 1. 4 张 dbg_*.png 全部存在
• 2. 至少 1 张含 VS Code "VARIABLES" / "WATCH" 面板特征（OCR）
• 3. analysis.md 字数 100-500
• 4. fix.patch 合法 unified diff，仅修改 pager/core.py（patch 中只能出现一个 +++ b/...core.py 头）
• 5. pytest 全部通过（pytest.txt 含 passed，无 failed/error）
• 6. 修复后 6 个 truth test case（含整除与非整除边界）全部通过
• 7. 4 张截图分辨率 ≥ 1024×600、文件 ≥ 5KB、md5 互不相同（防伪截图）
• 8. analysis.md 至少命中 2 个关键词（off-by-one / floor / ceil / 边界 / 余数 / mod）

Automated Checks

def grade(workspace_path=None, **kwargs) -> dict:
    """OPS_0 v2: weighted scoring + multi-tier hard gates + anti-cheat (md5/size/resolution)."""
    import json, hashlib, importlib.util
    from pathlib import Path
    try: import pytesseract; from PIL import Image
    except: pytesseract=None; Image=None
    gt = json.loads(Path("/tmp_workspace/gt/expected.json").read_text()) if Path("/tmp_workspace/gt/expected.json").exists() else {}
    rd = Path("/tmp_workspace/results")
    s = {}
    # ---- screenshots: existence + size + md5 uniqueness + min resolution ----
    req = gt.get("screenshots_required",[])
    md5s = {}; sizes = {}; resolutions = {}
    for n in req:
        p = rd/n
        if p.exists():
            try:
                b = p.read_bytes()
                md5s[n] = hashlib.md5(b).hexdigest()
                sizes[n] = len(b)
                if Image:
                    try:
                        with Image.open(p) as im: resolutions[n] = im.size
                    except: resolutions[n] = (0,0)
            except: pass
    for n in req:
        ok = (n in md5s) and sizes.get(n,0) >= 5*1024
        if Image and n in resolutions:
            w,h = resolutions[n]
            ok = ok and (w >= 1024 and h >= 600)
        s[f"img_{n}"] = 1.0 if ok else (0.5 if n in md5s else 0.0)
    unique = len(set(md5s.values()))
    s["img_unique"] = (unique/len(req)) if req else 0.0
    # ---- OCR keyword evidence (require >=2 distinct keywords across all imgs OR >=50% imgs hit) ----
    ocr_hit_imgs = 0; ocr_kw_set = set()
    if pytesseract and Image:
        for n in req:
            p = rd/n
            if not p.exists(): continue
            try:
                tx = pytesseract.image_to_string(Image.open(p)).upper()
                hits = [k for k in gt.get("ocr_keywords_debug_panel",[]) if k in tx]
                if hits:
                    ocr_hit_imgs += 1
                    ocr_kw_set.update(hits)
            except: pass
        s["debug_panel_ocr"] = (ocr_hit_imgs/len(req)) if req else 0.0
        s["debug_panel_ocr_kw_diversity"] = min(1.0, len(ocr_kw_set)/2.0)
    else:
        s["debug_panel_ocr"] = 0.0; s["debug_panel_ocr_kw_diversity"] = 0.0
    # ---- analysis ----
    a_path = rd/"analysis.md"
    a = a_path.read_text() if a_path.exists() else ""
    mn = gt.get("min_analysis_chars",100); mx = gt.get("max_analysis_chars",500)
    s["analysis_length"] = 1.0 if mn <= len(a) <= mx else (0.5 if a else 0.0)
    s["analysis_keywords"] = 1.0 if sum(1 for k in ["off-by-one","floor","ceil","边界","余数","mod"] if k in a.lower()) >= 2 else (0.5 if a else 0.0)
    # ---- patch: must touch only core.py, contain unified-diff hallmarks ----
    patch_path = rd/"fix.patch"
    patch = patch_path.read_text() if patch_path.exists() else ""
    plus_headers = [ln for ln in patch.splitlines() if ln.startswith("+++ ")]
    minus_headers = [ln for ln in patch.splitlines() if ln.startswith("--- ")]
    only_core = (len(plus_headers) == 1 and "core.py" in plus_headers[0] and len(minus_headers) == 1)
    s["valid_patch"] = 1.0 if (gt.get("buggy_file","core.py") in patch and "@@" in patch and only_core) else (0.5 if patch else 0.0)
    s["patch_uses_ceil"] = 1.0 if gt.get("expected_fix_pattern","math.ceil") in patch else 0.0
    # ---- pytest output ----
    pytest_path = rd/"pytest.txt"
    pt = pytest_path.read_text() if pytest_path.exists() else ""
    s["pytest_pass"] = 1.0 if ("passed" in pt and "failed" not in pt and "error" not in pt.lower()) else (0.3 if pt else 0.0)
    # ---- run 6 truth test cases against current core.py ----
    core_path = Path("/tmp_workspace/pager/core.py")
    if core_path.exists():
        try:
            spec = importlib.util.spec_from_file_location("pager_core", str(core_path))
            mod = importlib.util.module_from_spec(spec); spec.loader.exec_module(mod)
            ok=0; total=0
            for tc in gt.get("test_cases",[]):
                total+=1
                try:
                    if mod.total_pages(*tc["args"]) == tc["expect"]: ok+=1
                except: pass
            s["test_cases_pass"] = ok/max(total,1)
        except Exception:
            s["test_cases_pass"] = 0.0
    else:
        s["test_cases_pass"] = 0.0
    # ---- weighted aggregation: core delivery 60% / GUI evidence 30% / aux 10% ----
    img_keys = [f"img_{n}" for n in req]
    imgs_avg = sum(s.get(k,0) for k in img_keys)/max(len(img_keys),1)
    core_score = (
        0.45 * s.get("test_cases_pass",0) +
        0.25 * s.get("valid_patch",0) +
        0.15 * s.get("patch_uses_ceil",0) +
        0.15 * s.get("pytest_pass",0)
    )
    gui_score = (
        0.40 * imgs_avg +
        0.20 * s.get("img_unique",0) +
        0.25 * s.get("debug_panel_ocr",0) +
        0.15 * s.get("debug_panel_ocr_kw_diversity",0)
    )
    aux_score = 0.5*s.get("analysis_length",0) + 0.5*s.get("analysis_keywords",0)
    base = 0.6*core_score + 0.3*gui_score + 0.1*aux_score
    # ---- multi-tier hard gates ----
    imgs_present = sum(1 for n in req if n in md5s)
    if s.get("test_cases_pass",0) < 1.0:
        base = min(base, 0.45)
    if s.get("valid_patch",0) < 1.0:
        base = min(base, 0.5)
    if imgs_present < 3:
        base = min(base, 0.35)
    if s.get("img_unique",0) < 0.75:  # too many duplicate screenshots = cheat
        base = min(base, 0.4)
    if pytesseract and s.get("debug_panel_ocr",0) < 0.5:
        base = min(base, 0.55)
    if not pytesseract:  # no VLM/OCR → cap so blind runs can't max out
        base = min(base, 0.6)
    s["overall_score"] = round(base, 3)
    return s

Workspace Path

workspace/OPS/task_0_vscode_debugger_offbyone

Skills

Env

Warmup

# VS Code / python3 / pytest / pillow / tesseract are baked into v3_eyeson_apps_20260424.
# Only ensure pytesseract Python binding (sometimes missing) and prepare results dir.
pip install -q --break-system-packages pytesseract 2>/dev/null || pip install -q pytesseract 2>/dev/null || true
mkdir -p /tmp_workspace/results || true