Gjalla Test Audit

Test Audit

Deep review of test suite quality to find tests that give false confidence, encode bugs, duplicate coverage, or are so heavily mocked they can't catch real regressions.

Process

At a high level, you'll follow the steps below, then cross-reference load-bearing code with test imports. Importance can be ranked by production blast radius (which code is most depended-on — if you use gjalla, impact and change history surface this), recent bug history, and how deterministic the failure mode is.

Phase 1: Orient

Understand the project's test infrastructure before diving in.

1. Map test structure: Find all test directories, count files per directory, identify naming conventions (.test.ts, .pglite.test.ts, .integration.test.ts, etc.)
2. Identify test layers: Which tests use real databases (PGlite, SQLite)? Which mock the ORM? Which mock at the service boundary? Which use @vitest-environment node vs jsdom?
3. Identify critical components: What are the security boundaries, data access layers, and core business logic? These are where false confidence is most dangerous.

Phase 2: Hunt for anti-patterns

Launch parallel investigations across test layers. For each test file, read BOTH the test AND the source code it claims to test. The anti-patterns to find:

Anti-pattern 1: Reimplemented logic tests

Tests that never import the real code. Instead they redefine the logic inline and test their own copy. Signals:

• @vitest-environment node with no component/hook imports
• Local functions named simulate* or handle* that mirror source code
• Test file has zero imports from src/ or source directories

These tests will NEVER catch a regression because they don't exercise the real code.

Anti-pattern 2: Tautological mock tests

Tests that mock the entire database/ORM chain with hardcoded returns, then assert those same hardcoded values. Signals:

• mockReturnValue / mockResolvedValue on db.select().from().where() chains
• Queue-based mock infrastructure (_setSelectQueue, pushSelectResult)
• Assertions like expect(result).toEqual(mockReturnValue) where mockReturnValue is what the mock was set up to return
• Builder pattern mocks where .from(), .where(), .innerJoin() all ignore their arguments

Key test: Could a bug in the real code (wrong table, wrong column, wrong WHERE clause, wrong JOIN) cause this test to fail? If no, the test is tautological.

Anti-pattern 3: Tests encoding wrong behavior

Tests whose assertions verify incorrect behavior that happens to match buggy source code. Signals:

• Test fixtures using field names that don't match the source (e.g., test uses assignedTier but source reads subscriptionTier)
• Inconsistent thresholds between services tested independently
• Mock return values that paper over logic the test claims to verify
• Test names that say one thing but assert another (e.g., "returns 403" but asserts toBe(404))

Key test: Does the test's mock data match what real upstream code actually produces? Or was it hand-crafted to match the (possibly buggy) function under test?

Anti-pattern 4: Redundant companion tests

Tests that are fully covered by a more rigorous companion file. Signals:

• A .test.ts file that mocks the DB alongside a .pglite.test.ts file that tests real SQL for the same class
• An "integration" test that mocks at the same level as the "unit" test
• Multiple test files for the same source file with overlapping describe/it blocks

Anti-pattern 5: Placeholder tests

• expect(true).toBe(true)
• Tests with descriptive names but no real assertions
• Tests that call a mock and then assert the mock was called (tautology)

Phase 3: Classify findings

Organize findings into tiers:

Phase 4: Report

Present findings as a structured report with:

1. Tests encoding bugs (highest priority) - these are masking real production issues
2. Tier 1-3 deletions with file paths and line counts
3. Tier 4 gaps - areas where coverage will honestly drop and needs real tests
4. Impact analysis: what gets more robust, what bugs may surface, coverage impact

Phase 5: Clean up (if approved)

Execute in waves, running tests between each:

1. Delete Tier 1 files (zero real code tested)
2. Remove Tier 2 tautological sections
3. Remove Tier 3 redundant tests (where companion exists)
4. Fix tests encoding wrong behavior + fix source bugs
5. Run coverage to identify honest gaps
6. Write real tests for Tier 4 gaps (PGlite for DB-dependent code)

Principles

• A test that can't fail is worse than no test. It inflates confidence without providing protection.
• Mock at boundaries, not internals. Mock external services (Supabase, Stripe, GitHub API). Don't mock your own database layer when you can use PGlite.
• Round-trip tests catch key mismatches. When function A writes data that function B reads, test them together using A's actual output as B's input. Hand-crafted fixtures can silently encode bugs.
• Coverage numbers lie when tests are tautological. A line is not "covered" if the mock intercepted every meaningful operation. Honest coverage comes from tests that exercise real code paths.
• Delete before you write. Remove false confidence first so the coverage report becomes an honest map of where you're strong and where you're exposed. Then fill gaps.