AI-Assisted Testing

中文版： 见技能 ai-assisted-testing 。

Prompts: see prompts/ai-assisted-testing_EN.md in this directory.

When to Use

• User mentions AI-assisted testing, intelligent testing, or AI testing

• Need to leverage AI to improve testing efficiency and quality

• Trigger: e.g. "Use AI to generate test data" or "AI analyze defect root cause"

Output Format Options

This skill defaults to Markdown output. For other formats, specify at the end of your request.

How to Use

• Open the relevant file in this directory's prompts/ and copy the content below the dashed line.

• Append your requirements and context (business flow, environment, constraints, acceptance criteria).

• If you need non-Markdown output, append the request sentence from output-formats.md at the end.

Reference Files

• prompts/ai-assisted-testing_EN.md — AI-assisted testing prompts

• output-formats.md — Format specifications

Code Examples

• AI Testing Toolkit (Planned) - AI-assisted testing tools and scripts

Common Pitfalls

• ❌ Completely relying on AI → ✅ AI assists, human decides

• ❌ Not validating AI output → ✅ Verify and review AI results

• ❌ Ignoring data quality → ✅ Ensure training data quality

• ❌ Missing feedback loop → ✅ Continuously optimize AI models

Best Practices

1. AI-Assisted Testing Scenarios

Test Data Generation:

• Boundary value generation

• Exception data generation

• Large-scale data generation

• Personalized data generation

Defect Analysis:

• Root cause analysis

• Similar defect identification

• Defect prediction

• Impact analysis

Test Optimization:

• Test case prioritization

• Test suite optimization

• Regression test selection

• Resource allocation optimization

Intelligent Recommendations:

• Test case recommendations

• Test tool recommendations

• Test strategy recommendations

• Improvement suggestions

2. AI Tool Selection

Tool Type Purpose Example Tools

Code Generation Generate test code GitHub Copilot, ChatGPT

Data Generation Generate test data Faker, GPT

Defect Analysis Analyze defect patterns ML models

Test Optimization Optimize test strategy AI algorithms

3. AI-Assisted Workflow

AI-Assisted Testing Process

1. Requirements Analysis

   • AI extracts test points
   • Human review and confirmation

2. Test Case Design

   • AI generates case drafts
   • Human optimizes and refines

3. Data Preparation

   • AI generates test data
   • Human validates data

4. Execute Tests

   • Automated execution
   • AI analyzes results

5. Defect Analysis

   • AI analyzes root cause
   • Human confirms fix

6. Continuous Improvement

   • Collect feedback
   • Optimize AI models

Troubleshooting

Issue 1: AI-generated content inaccurate

Solution:

• Provide more detailed context

• Use examples to guide AI

• Iteratively optimize prompts

• Human review and correction

Issue 2: High AI tool costs

Solution:

• Prioritize open-source tools

• Use AI only in critical scenarios

• Batch processing to reduce costs

• Evaluate ROI

Related Skills: test-case-writing-en, bug-reporting-en, test-strategy-en.

Target Audience

• QA engineers and developers executing this testing domain in real projects

• Team leads who need structured, reproducible testing outputs

• AI users who need fast, format-ready deliverables for execution and reporting

Not Recommended For

• Pure production incident response without test scope/context

• Decisions requiring legal/compliance sign-off without expert review

• Requests lacking minimum inputs (scope, environment, expected behavior)

Critical Success Factors

• Provide clear scope, environment, and acceptance criteria before generation

• Validate generated outputs against real system constraints before execution

• Keep artifacts traceable (requirements -> test points -> defects -> decisions)

Output Templates and Parsing Scripts

• Template directory: output-templates/

• template-word.md (Word-friendly structure)

• template-excel.tsv (Excel paste-ready)

• template-xmind.md (XMind-friendly outline)

• template-json.json

• template-csv.csv

• template-markdown.md

• Parser scripts directory: scripts/

• Parse (generic): parse_output_formats.py

• Parse (per-format): parse_word.py , parse_excel.py , parse_xmind.py , parse_json.py , parse_csv.py , parse_markdown.py

• Convert (generic): convert_output_formats.py

• Convert (per-format): convert_to_word.py , convert_to_excel.py , convert_to_xmind.py , convert_to_json.py , convert_to_csv.py , convert_to_markdown.py

• Batch convert: batch_convert_templates.py (outputs into artifacts/ )

Examples:

python3 scripts/parse_json.py output-templates/template-json.json python3 scripts/parse_markdown.py output-templates/template-markdown.md python3 scripts/convert_to_json.py output-templates/template-markdown.md python3 scripts/convert_output_formats.py output-templates/template-json.json --to csv python3 scripts/batch_convert_templates.py --skip-same