ai-generated-ut-code-review

name	ai-generated-ut-code-review
description	Use when reviewing or scoring AI-generated unit tests/UT code, especially when coverage, assertion effectiveness, or test quality is in question and a numeric score, risk level, or must-fix checklist is needed

AI UT Code Review

Overview

Review AI-generated unit tests for effectiveness, coverage, assertions, negative cases, determinism, and maintainability. Output a 0-10 score, a risk level, and a must-fix checklist. Overall line coverage must be >= 80%; otherwise risk is at least High.

When to Use

• AI-generated UT/test code review or quality evaluation
• Need scoring, risk level, or must-fix checklist
• Questions about coverage or assertion validity

Workflow

1. Confirm tests target the intended business code and key paths.
2. Check overall line coverage (>= 80% required).
3. Inspect assertions for behavioral validity; flag missing/ineffective assertions.
4. Verify negative/edge cases and determinism (no env/time dependency).
5. Score by rubric, assign risk, list must-fix items with evidence.

Scoring (0-10)

Each dimension 0-2 points. Sum = total score.

Dimension	0	1	2
Coverage	< 80%	80%+ but shallow	80%+ and meaningful
Assertion Quality	No/invalid assertions	Some weak assertions	Behavior-anchored assertions
Negative & Edge	Missing	Partial	Comprehensive
Data & Isolation	Flaky/env-dependent	Mixed	Deterministic, isolated
Maintainability	Hard to read/modify	Mixed quality	Clear structure & naming

Risk Levels

• Blocker: Coverage < 80% AND key paths untested, or tests have no meaningful assertions
• High: Coverage < 80% OR assertions largely ineffective
• Medium: Coverage OK but weak edge cases or fragile design
• Low: Minor improvements

Must-Fix Checklist

• Overall line coverage >= 80%
• Each test has at least one behavior-relevant assertion
• Negative/exception cases exist for core logic
• Tests are deterministic and repeatable

AI-Generated Test Pitfalls (Check Explicitly)

• No assertions or assertions unrelated to behavior (e.g., only not-null)
• Over-mocking hides real behavior
• Only happy-path coverage
• Tests depend on time/network/env
• Missing verification of side effects

Output Format (Required, Semi-fixed)

• Score: x/10 — Coverage x, Assertion Quality x, Negative & Edge x, Data & Isolation x, Maintainability x
• Risk: Low/Medium/High/Blocker — 简述原因（1 行）
• Must-fix:
  • [动作 + 证据]
  • [动作 + 证据]
• Key Evidence:
  • 引用具体测试用例名或覆盖率报告摘要（1-2 条）
• Notes:
  • 最小修复建议或替代方案（1-2 行）

Rules:

• 覆盖率 < 80% 风险至少 High，并必须列入 Must-fix
• 无断言/无效断言直接提升风险级别，必须列入 Must-fix
• 至少 2 条证据；证据不足需说明并降分

Common Mistakes

• 仅报告覆盖率，不评价断言有效性
• 把日志输出当成断言
• 忽略失败路径/异常路径

Example (Concise)

Score: 5/10 (Coverage 1, Assertion 0, Negative 1, Data 2, Maintainability 1) Risk: High Must-fix:

• Tests for parseConfig() contain no behavior assertions (only logs)
• No negative cases for malformed input Key Evidence:
• parseConfig() tests only assert no crash
• Coverage report shows 62% lines Notes:
• Add assertions on outputs and side effects; add invalid input tests.