AI UT Code Review

SkillDev tools

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

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the AI UT Code Review skill

What this skill tells your AI

The instructions your AI receives, as published by openharmonyinsight/openharmony-skills in skills/ai-generated-ut-code-review/SKILL.md and read by ahel’s 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.

Signals

GitHub stars
34
Forks
7
Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
ai-generated-ut-code-review
Source
github.com/openharmonyinsight/openharmony-skills