Course Objective & Scope
When you hand an existing function to an LLM and ask it to 'write unit tests', it typically mirrors the implementation—including its bugs—and inflates line coverage with weak assertions. This course teaches developers how to drive AI test generation from specifications and invariants so tests actually catch defects.
Spec-first TDD workflows, boundary-value and equivalence partitioning, avoiding over-mocking, property-based testing (Hypothesis, fast-check, jqwik), integration testing with Testcontainers, and measuring test effectiveness via mutation testing (Stryker, mutmut, PIT) are practised hands-on.
Who Should Attend?
- Backend, frontend and full-stack software developers
- SDETs and software engineers building test automation
- Tech leads responsible for engineering quality
- Prerequisite: hands-on experience with at least one unit test framework (JUnit, xUnit, pytest, Vitest/Jest, etc.)
Key Takeaways
- Generate tests from specifications rather than implementation to expose logic bugs
- Cover boundary values, null/empty inputs, timezones, overflows and concurrency edge cases systematically
- Replace brittle mock-heavy tests with clean fakes, contract tests and Testcontainers setups
- Verify domain invariants across hundreds of generated inputs using property-based testing
- Eliminate weak assertions and false confidence using mutation testing scores
Curriculum
The AI Testing Trap: Line Coverage vs. Defect Detection
- Why implementation-derived tests rubber-stamp existing bugs (tautological tests)
- Generating tests from specs, type signatures and acceptance criteria (AI-assisted TDD)
Boundary Values, Negative Scenarios and Edge-Case Discovery
- Equivalence classes, boundary analysis, Unicode/locale, timezone and rounding traps
- Building readable table-driven / parameterised test suites
Mocking Discipline, Integration and Contract Tests
- Replacing brittle mock-heavy tests with in-memory fakes and Testcontainers
- Automated generation of OpenAPI/JSON Schema and API contract tests
Property-Based Testing and Fuzzing
- Defining input generators (arbitraries) and domain invariants with Hypothesis, fast-check or jqwik
- Hunting corner cases in parsers, serialisers and calculation engines
Validating Test Suite Strength with Mutation Testing
- Running Stryker, mutmut or PIT to find surviving mutants
- Feeding surviving mutants back to the coding agent to strengthen assertions
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