Unit Test Suite Generation for Legacy Code AI Prompt
Writing unit tests for legacy code is hard. You’re missing docs, function behavior is unclear, and risk of breaking production is high. You need targeted tests that boost coverage without rewriting half the codebase. A strong prompt turns your scattered notes into a precise testing plan: what to test, how to mock, and which edge cases matter.
AskSmarter.ai guides you with clarifying questions—language, framework, coverage goals, constraints, risk areas—then builds a structured prompt that yields maintainable tests on the first pass. You’ll spend less time re-running AI guesses and more time shipping safe improvements.
Use this prompt to generate a focused test suite that increases confidence, documents intent, and prevents regressions—especially when you can’t change the original code yet.
The transformation
Before — Vague prompt
Write unit tests for this old module. Aim for good coverage.
After — Optimized prompt
Role: Senior Software Test Engineer
Goal: Generate a maintainable unit test suite for a legacy module.
Context:
- Language/Framework: Python 3.11, pytest, unittest.mock
- Target file: payments/processor.py (paste code below)
- Coverage: minimum 85% lines/branches
- Constraints: don’t modify source; avoid network/DB calls; use fakes
- Risks: timezone math, retry logic, rounding errors
Instructions:
- Propose a test plan first (cases, boundaries, mocks, fixtures).
- Write pytest tests with clear names and AAA structure.
- Include examples for success, failure, and edge inputs.
- Show how to mock HTTP and clock.
- Provide a coverage checklist and next steps.
Code: [paste processor.py here]
Why this works
The optimized prompt works because it adds the missing context and structure that AI needs to generate useful tests.
- Clarity: It defines the role, goal, and explicit success criteria (85% coverage), so the AI knows what “good” looks like.
- Context: It specifies Python 3.11, pytest, and unittest.mock, the file path, and known risk areas (timezone/retry/rounding). That steers test design toward real defects.
- Structure: It requests a test plan first, then code, with the AAA pattern, named cases, and a coverage checklist. This reduces rework and makes outputs auditable.
- Constraints: It forbids source changes and external calls, and requires fakes/mocks. That keeps tests hermetic and CI-friendly.
- Maintainability: It asks for fixtures, mocking guidance, and next steps, which helps teams extend the suite.
AskSmarter.ai reaches this specificity by asking targeted questions about tech stack, constraints, risk hotspots, and coverage goals—details users often skip. The result is a prompt that produces reliable, review-ready tests, not generic examples.
When to use this prompt
Engineering Teams Refactoring Legacy Services
Increase safety before refactors by generating tests around fragile modules. Catch regressions without changing production code.
Product Managers Requiring Release Confidence
Set measurable coverage targets on critical paths and get auditable test plans that support go/no-go decisions.
Customer Success Reproducing Edge Bugs
Create focused tests for reported issues (timezones, retries) to validate fixes and prevent recurrences.
Researchers Benchmarking Code Quality
Generate consistent test suites to compare module reliability and mutation score improvements across versions.
DevOps Owners Hardening CI Pipelines
Add deterministic, mocked tests that run fast in CI, improving feedback loops and deployment confidence.
Pro tips
- 1
Specify mutation testing or branch coverage if quality matters more than line count.
- 2
List concrete risk areas (e.g., time math, retries, floating-point) to focus edge cases.
- 3
Provide interface contracts or sample inputs/outputs to clarify expected behavior.
- 4
State mocking policies (e.g., forbid real I/O, use fakes for HTTP/clock) to keep tests reliable.
More coding & technical examples
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Build a prompt for your situation
This example shows the pattern. AskSmarter.ai guides you to create prompts tailored to your specific context, audience, and goals.