Testing Framework & Continuous Execution · 40 min · Claude Code · GPT-4 · Cursor

Prompt Library: Testing Prompts

15+ reusable prompts for test generation, edge case discovery, adversarial testing, mutation analysis, property derivation, and concurrency testing.

Hiring signal: Engineers who build reusable prompt libraries for testing demonstrate prompt engineering and testing automation expertise.

What you will learn

Apply 7 testing prompts: test generation, edge case discovery, adversarial testing, mutation analysis, property derivation, concurrency testing, regression test generation
Structure prompts with context, task, constraints, and expected output format
Use prompts in the continuous testing loop for automated test generation
Evaluate prompt effectiveness by measuring test coverage and mutation score improvements

The Problem

Testing AI-generated code requires generating tests, finding edge cases, and verifying behavior. Doing this manually is slow and inconsistent. Prompt libraries provide reusable, tested prompts that produce consistent, high-quality testing outputs.

The 7 Testing Prompts

#	Prompt	Purpose	Input	Output
1	Test Generation	Generate unit tests for a function	Function code + signature	pytest test file
2	Edge Case Discovery	Find edge cases AI code misses	Function code + docstring	List of edge cases with test code
3	Adversarial Testing	Generate inputs that break the function	Function code + type hints	Failing test cases
4	Mutation Analysis	Suggest mutations and verify tests catch them	Function code + test code	Mutation report
5	Property Derivation	Derive properties from function behavior	Function code + type hints	Hypothesis properties
6	Concurrency Testing	Generate concurrent test scenarios	Function code + shared state	Concurrent test code
7	Regression Test Generation	Generate regression tests from a bug report	Bug report + fix diff	Regression test code

Prompt structure
Each prompt follows a 4-part structure: (1) Context: What the AI needs to know (function code, type hints, docstring). (2) Task: What the AI should do (generate tests, find edge cases, derive properties). (3) Constraints: What the AI must follow (use pytest, cover edge cases, include assertions). (4) Output format: How the AI should format its response (markdown code blocks, JSON, specific file format). This structure ensures consistent, high-quality outputs across different AI models and different invocations.

Why is a prompt library better than ad-hoc prompting for testing?

A prompt library is better than ad-hoc prompting for testing for 5 reasons: (1) Consistency: Ad-hoc prompts vary every time -- different engineers write different prompts for the same task, producing different quality tests. A prompt library ensures the same prompt is used every time, producing consistent results. For example, the 'Test Generation' prompt always produces pytest tests with edge cases, assertions, and docstrings. An ad-hoc prompt might produce tests without edge cases, without assertions, or in a different format. (2) Quality: Library prompts are tested and refined. The 'Edge Case Discovery' prompt has been run against 100+ functions and refined to produce comprehensive edge cases. An ad-hoc prompt written on the spot hasn't been tested -- it might miss important edge cases or produce irrelevant ones. (3) Efficiency: With a prompt library, you don't need to think about how to phrase a prompt. You look up 'Test Generation', fill in the function code, and run it. This takes 30 seconds. Writing an ad-hoc prompt from scratch takes 5-10 minutes and may need iteration. (4) Shareability: A prompt library is version-controlled and shared. New team members use the same prompts as experienced engineers. This ensures consistent testing practices across the team. With ad-hoc prompting, each engineer has their own prompts (or none), leading to inconsistent testing. (5) Measurability: You can track which prompts produce the best tests. 'The Test Generation prompt produced tests with 92% coverage and 85% mutation score. The Edge Case Discovery prompt found 15 edge cases, 3 of which revealed bugs.' This data helps you improve prompts over time. With ad-hoc prompting, you can't measure effectiveness because each prompt is different."

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