跳到正文
cccskills
免费在 GitHub 公开

bug-reproduction-test-generator

Automatically generates executable tests that reproduce reported bugs from issue reports and code repositories. Use when users need to: (1) Create a test that reproduces a bug described in an issue report, (2) Generate failing tests from bug descriptions, stack traces, or error messages, (3) Validate bug reports by creating reproducible test cases, (4) Convert issue reports into executable regression tests. Takes a repository and issue report as input and produces test code that reliably triggers the reported bug.

中文简介正在准备中。当前显示原文说明。

查看安装方法

包含的文件(1)

  • SKILL.md6.9 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Bug Reproduction Test Generator

Generate executable tests that reproduce reported bugs based on issue reports and code repositories.

Workflow

Follow these steps to generate a bug reproduction test:

1. Analyze the Issue Report

Extract key information from the issue report:

  • Symptoms: What goes wrong? (incorrect output, exception, crash, assertion failure, unexpected behavior)
  • Affected components: Which modules, classes, or functions are involved?
  • Triggering conditions: What inputs, states, or sequences trigger the bug?
  • Stack traces: If provided, identify the call chain and failure point
  • Expected vs. actual behavior: What should happen vs. what actually happens?

2. Inspect the Repository

Identify relevant code and context:

  • Locate the affected components mentioned in the issue
  • Find entry points (public APIs, main functions, test fixtures)
  • Understand dependencies and required setup
  • Identify the test framework used (pytest, unittest, JUnit, Jest, etc.)
  • Check existing test patterns for consistency

3. Generate the Reproduction Test

Create a minimal, focused test that:

Test structure:

  • Uses the repository's existing test framework and conventions
  • Sets up minimal preconditions needed to trigger the bug
  • Executes the code path that triggers the bug
  • Asserts the symptom described in the issue report

Assertions:

  • For exceptions: Assert the exception type and message match the report
  • For incorrect output: Assert actual output matches the reported incorrect behavior
  • For crashes: Assert the crash occurs at the expected point
  • For assertion failures: Reproduce the failing assertion

Documentation:

  • Add inline comments explaining the reproduction logic
  • Reference the issue number/URL in the test name or docstring
  • Document any assumptions made due to underspecified details

4. Output Format

Provide:

  1. Executable test code in the appropriate language and framework
  2. Inline comments explaining how the test reproduces the bug
  3. Markdown summary including:
    • How the test reproduces the issue
    • Which symptoms it validates
    • Any assumptions made
    • Instructions for running the test

Example Workflow

Issue Report:

Title: Division by zero in calculate_average()
Description: When calling calculate_average([]) with an empty list,
the function crashes with ZeroDivisionError instead of returning 0.

Stack trace:
  File "stats.py", line 15, in calculate_average
    return sum(values) / len(values)
ZeroDivisionError: division by zero

Generated Test (Python/pytest):

import pytest
from stats import calculate_average

def test_calculate_average_empty_list_reproduction():
    """
    Reproduces bug: calculate_average([]) raises ZeroDivisionError
    Issue: #123

    Expected: Should return 0 for empty list
    Actual: Raises ZeroDivisionError
    """
    # Trigger the bug with empty list input
    with pytest.raises(ZeroDivisionError):
        result = calculate_average([])

    # This test currently passes (reproduces the bug)
    # After fix, change to: assert calculate_average([]) == 0

Summary:

## Bug Reproduction Test

**Issue**: Division by zero in calculate_average()

**How it reproduces the bug:**
- Calls `calculate_average([])` with an empty list
- Asserts that ZeroDivisionError is raised (the buggy behavior)

**Symptoms validated:**
- Exception type: ZeroDivisionError
- Location: stats.py line 15

**Assumptions:**
- The function should return 0 for empty lists (common convention)

**Running the test:**
```bash
pytest test_stats.py::test_calculate_average_empty_list_reproduction

After the bug is fixed: Replace the pytest.raises assertion with:

assert calculate_average([]) == 0

## Language-Specific Patterns

### Python (pytest/unittest)

```python
import pytest

def test_bug_reproduction_issue_123():
    """Reproduces bug #123: [brief description]"""
    # Setup: Create conditions that trigger the bug

    # Execute: Run the code that exhibits the bug

    # Assert: Verify the buggy behavior occurs
    with pytest.raises(ExpectedException):
        buggy_function()

Java (JUnit)

@Test
public void testBugReproduction_Issue123() {
    // Reproduces bug #123: [brief description]

    // Setup: Create conditions that trigger the bug

    // Execute and Assert: Verify the buggy behavior
    assertThrows(ExpectedException.class, () -> {
        buggyMethod();
    });
}

JavaScript (Jest)

test('reproduces bug #123: [brief description]', () => {
  // Setup: Create conditions that trigger the bug

  // Execute and Assert: Verify the buggy behavior
  expect(() => {
    buggyFunction();
  }).toThrow(ExpectedException);
});

Constraints

  • Do not modify production code - Only create test code
  • Do not assume fixes - Test the buggy behavior, not the expected correct behavior (unless explicitly stated in the issue)
  • Document assumptions - If the issue is underspecified, state assumptions clearly
  • Prefer minimal tests - Focus on isolating the bug, avoid unnecessary setup
  • Match existing patterns - Follow the repository's test conventions and style

Handling Underspecified Issues

When the issue report lacks details:

  1. State assumptions explicitly in test comments
  2. Document what's unclear in the summary
  3. Provide multiple test variants if multiple interpretations are possible
  4. Ask clarifying questions if critical information is missing

Example:

def test_bug_reproduction_issue_456():
    """
    Reproduces bug #456: Null pointer exception in processData()

    ASSUMPTION: The bug occurs when input is null (not specified in issue)
    ASSUMPTION: Using default configuration (not specified in issue)
    """
    # Test with null input (assumed trigger)
    with pytest.raises(NullPointerException):
        processData(None)

Tips for Effective Reproduction Tests

  1. Start simple - Begin with the most direct path to trigger the bug
  2. Isolate the bug - Remove unrelated setup and assertions
  3. Make it deterministic - Avoid flaky conditions (timing, randomness)
  4. Reference the issue - Include issue number in test name and comments
  5. Verify it fails - Run the test to confirm it reproduces the bug
  6. Plan for the fix - Comment on how the test should change after the bug is fixed

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

Applies abstract interpretation using different abstract domains (intervals, octagons, polyhedra, sign, congruence) to statically analyze program variables and infer invariants, value ranges, and relationships. Use when analyzing program properties, inferring loop invariants, detecting potential errors, or understanding variable relationships through static analysis.

中文简介正在准备中。当前显示原文说明。

ArabelaTso/Skills-4-SE2582026年8月21日 更新

Uses abstract interpretation to automatically infer loop invariants, function preconditions, and postconditions for formal verification. Generates invariants that capture program behavior and support correctness proofs in Dafny, Isabelle, Coq, and other verification systems. Use when adding formal specifications to code, generating verification conditions, inferring contracts for functions, or discovering loop invariants for proofs.

中文简介正在准备中。当前显示原文说明。

ArabelaTso/Skills-4-SE2582026年8月21日 更新

Performs abstract interpretation over source code to infer possible program states, variable ranges, and data properties without executing the program. Reports potential runtime errors including out-of-bounds accesses, null dereferences, type inconsistencies, division by zero, and integer overflows. Use when analyzing code for potential runtime errors, performing static analysis, checking safety properties, or verifying program behavior without execution.

中文简介正在准备中。当前显示原文说明。

ArabelaTso/Skills-4-SE2582026年8月21日 更新

Performs abstract interpretation to produce summarized execution traces and high-level program behavior representations. Highlights key control flow paths, variable relationships, loop invariants, function summaries, and potential runtime states using abstract domains (intervals, signs, nullness, etc.). Use when analyzing program behavior, understanding execution paths, computing loop invariants, tracking variable ranges, detecting potential runtime errors, or generating program summaries without concrete execution.

中文简介正在准备中。当前显示原文说明。

ArabelaTso/Skills-4-SE2582026年8月21日 更新

Create ACSL (ANSI/ISO C Specification Language) formal annotations for C/C++ programs. Use this skill when working with formal verification, adding function contracts (requires/ensures), loop invariants, assertions, memory safety annotations, or any ACSL specifications. Supports Frama-C verification and generates comprehensive formal specifications for C/C++ code.

中文简介正在准备中。当前显示原文说明。

ArabelaTso/Skills-4-SE2582026年8月21日 更新

CLI-based browser automation with persistent page state using ref-based element interaction. Use when users ask to navigate websites, interact with web pages, fill forms, take screenshots, test web applications, or extract information from web pages.

中文简介正在准备中。当前显示原文说明。

ArabelaTso/Skills-4-SE2582026年8月21日 更新

查看 ArabelaTso 的全部技能

报告此技能的问题