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Evaluation suite

As explained in Coding with AI

A collection of software tests maintained and executed together to evaluate an application or component.

A test suite is a maintained collection of software tests run together to check reliability, security, expected behavior, and protection against regressions. It turns testing from rushed repetition into an investment that catches bugs early and documents how code should behave. section 8.0

A practical AI-assisted test-suite workflow can:

  • Analyze existing code, infer method signatures, and generate repetitive test boilerplate. section 8.0
  • Produce unittest or pytest tests, including cases for edge conditions that developers may overlook. section 8.0 section 1.7.2
  • Use an in-memory database to isolate tests from persistent application data. section 8.0
  • Generate a first draft with GitHub Copilot, Tabnine, or Blackbox AI, then evaluate and revise it rather than trusting it unchanged. section 8.0 section 1.11

Prompt-led test generation

Give an AI coding tool the relevant Python code and request a unittest or pytest suite covering normal behavior, failures, and overlooked edge cases. Review the generated setup, assertions, and database handling; correct weak coverage or unnecessary complexity; then run the suite as the application evolves. section 8.0 section 1.11

How Coding with AI covers it

How deep it goes
Explains suite generation, isolation, prompting, and evaluation, but does not detail individual testing algorithms or frameworks internally.
Examples
Demonstrates GitHub Copilot, Tabnine, Blackbox AI, unittest, pytest, and isolated in-memory testing environments.
What it assumes
Assumes Python development context and introduces automated testing as a practical workflow for both new and experienced developers.
What you can do after
Build and validate Python test suites with generative AI while reviewing coverage, correctness, and maintainability.
How it connects
Connects test suites with generative AI, prompt engineering, edge cases, unittest, pytest, and in-memory databases.

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This explainer was generated by Manning's knowledge graph from the text of Coding with AI. The section links point to the parts of the book it draws on.