ai-generated-ut-code-review

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

Safety Notice

This listing is imported from skills.sh public index metadata. Review upstream SKILL.md and repository scripts before running.

Copy this and send it to your AI assistant to learn

Install skill "ai-generated-ut-code-review" with this command: npx skills add openharmonyinsight/openharmony-skills/openharmonyinsight-openharmony-skills-ai-generated-ut-code-review

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.

Dimension012
Coverage< 80%80%+ but shallow80%+ and meaningful
Assertion QualityNo/invalid assertionsSome weak assertionsBehavior-anchored assertions
Negative & EdgeMissingPartialComprehensive
Data & IsolationFlaky/env-dependentMixedDeterministic, isolated
MaintainabilityHard to read/modifyMixed qualityClear 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.

Source Transparency

This detail page is rendered from real SKILL.md content. Trust labels are metadata-based hints, not a safety guarantee.

Related Skills

Related by shared tags or category signals.

Coding

ai-generated-business-code-review

No summary provided by upstream source.

Repository SourceNeeds Review
Coding

oh-pdd-code-generator

No summary provided by upstream source.

Repository SourceNeeds Review
Coding

code-checker

No summary provided by upstream source.

Repository SourceNeeds Review
Coding

gitcode-pr

No summary provided by upstream source.

Repository SourceNeeds Review