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Faculty of Software, Web, and Product Engineering · Module F1-SW-02

Test-Driven Development Discipline

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Faculty 1 — Software, Web, and Product Engineering

Module F1-SW-02: Test-Driven Development Discipline

Learning Objective

By the end of this module, you can write a failing test before writing implementation code, use the test suite as a specification rather than a validation afterthought, and diagnose failures without guessing — tracing from the assertion that failed back to the exact line of code responsible.

1. Tests Are Specifications, Not Safety Nets

The common framing is wrong: tests do not "make sure code works after you write it." That is verification, and it is the weakest use of tests. Tests written after implementation confirm only that the code does what you already believed it did — they offer no protection against wrong beliefs.

The stronger claim is this: a test is a specification expressed in code. It describes the exact observable behaviour required, before anything is implemented. The failing test is the requirement. The passing test is the evidence the requirement is met.

When you write tests after implementation, you face a systematic risk: you write tests that pass because they confirm the implementation, not because they confirm the specification. The test is shaped by the code you wrote, not by the behaviour you needed. You miss cases the implementation misses, because your mental model of the problem is the implementation.

When you write a failing test first, the test is shaped by your understanding of the requirement. If the implementation does not satisfy it, the test tells you so immediately.

The red-green-refactor cycle

Test-driven development is disciplined iteration:

  1. Red: Write one failing test for the next unit of behaviour. Run the suite. Confirm it fails for the right reason (the right assertion, not a syntax error or import failure).
  2. Green: Write the minimum code needed to make that test pass. Do not add behaviour that is not required to pass the current test.
  3. Refactor: Clean the implementation without changing behaviour. Run the suite. Confirm it is still green.

The cycle is short — one test at a time, one behaviour at a time. The discipline is the shortness: if you write three tests before writing any implementation, you have left the cycle. The tests are now a backlog, not a specification.

2. What to Test and What Not to Test

Testing everything is not the goal. Testing everything produces a test suite that is expensive to maintain, slow to run, and brittle to refactor. The question is: what is worth specifying in a test?

Test observable behaviour, not internal structure. A test that asserts the internal state of a private variable is testing implementation, not behaviour. When the implementation changes — even correctly — the test breaks. Tests that break on correct refactors are noise, not signal. Write tests that specify what comes out of a function given what goes in.

Test the contract, not the collaborator. When a function delegates to another function, test the contract of the outer function. Mock the collaborator only if it is I/O (database, network, filesystem, clock) or if its real behaviour makes the test slow or non-deterministic. Do not mock collaborators because they are "complex" — that is the test evading the complexity, not the code handling it.

Test failure paths as deliberately as success paths. Error handling is behaviour. A function that should return 400 for a malformed input has a specification: given malformed input, return 400. Write that test.

Do not test implementation details of the framework. If you are using Next.js, do not write tests that verify that Next.js routes correctly — that is the framework's responsibility. Test your handler logic in isolation.

The three test levels and their cost ratios

  • Unit tests: Test one function in isolation, with dependencies mocked. Fast (milliseconds each). Should be most of your suite.
  • Integration tests: Test multiple real components together (e.g., your handler + real database). Slow (seconds each). Cover the contract between layers, not every code path.
  • End-to-end tests: Test the full system through a real interface. Very slow (tens of seconds each). Cover critical user paths, not edge cases.

A healthy suite is pyramid-shaped: many unit tests, some integration tests, few end-to-end tests. An inverted pyramid — many slow tests, few fast tests — produces a suite developers avoid running, which defeats its purpose.

3. Diagnosing Failures Without Guessing

When a test fails, the first instinct is to read the error message and guess the cause. This is the wrong workflow. Guessing introduces a second source of error — the guess — on top of the original defect. The correct workflow is to trace.

The trace protocol

  1. Read the exact assertion that failed. The error message tells you what was asserted and what was observed. Before anything else, understand what comparison failed — not what function is being tested, what the exact difference was.

  2. Identify the nearest controllable point above the failure. If the assertion is "expected 200, got 404," the nearest controllable point is the route handler. Not the framework, not the database — the handler you wrote.

  3. Add one observation point, then run again. Do not add multiple log statements or multiple breakpoints. Add one, at the nearest controllable point. Confirm whether the failure is above or below that point.

  4. Bisect inward. Once you know whether the failure is above or below your observation point, move the observation point inward. Repeat until you have localised the failure to a single function or expression.

  5. Fix the failure at its source. Do not fix symptoms. If step 4 reveals that the failure is caused by an incorrect argument being passed to a function, fix the call site — do not add a guard inside the function that compensates for incorrect input.

Distinguishing test failures from bugs

Some test failures indicate bugs in the code under test. Others indicate bugs in the test itself. Before changing implementation, verify that the test is correct: does it express the actual requirement? Is the mock returning the right value? Is the assertion testing what you intend?

A common failure mode is a test that asserts the wrong thing, passes in the wrong case, and fails in the right case — giving you a false sense of coverage. If a test passes before you have written any implementation, it is likely testing nothing.

The fastest way to check that a test is real: temporarily return a wrong value from the function being tested and confirm the test fails. If it does not, the test is not covering what you think it is.

4. The Discipline of Not Skipping

The fastest path to writing a new feature is to skip the tests. The fastest path to having no idea whether your system works is also to skip the tests.

Skipped tests are deferred specifications. They accumulate. After enough cycles of skipping, the test suite describes a system that no longer exists — it is the system that existed when the tests were written. The divergence is invisible until a production failure makes it visible.

The commitment required by test-driven development is not primarily technical. It is the commitment to treat the test suite as authoritative. The suite is the specification. If the suite says something is broken, it is broken. If the suite is wrong, fix the suite — do not accumulate a folklore of "tests you can ignore."

Practice Tasks

P-F1SW02-1: Red before green (Deterministic)

A function clamp(value, min, max) should return value if it is within [min, max], return min if it is below, and return max if it is above.

Write the three failing tests that specify this behaviour before writing the implementation. For each test, state:

  • The input values
  • The expected return value
  • The name you would give the test

Your answer is correct if:

  • All three cases (within range, below min, above max) are covered by distinct tests
  • Each expected value matches the specification exactly
  • Each test name describes the scenario being specified (not the implementation)

P-F1SW02-2: Identifying what to mock (Deterministic)

A route handler fetches a row from a database, formats it, and returns JSON. The test file mocks the database client, the file system, the external email service, and the logger.

Which mocks are justified? Which should be removed and why?

Your answer is correct if:

  • You identify the database client mock as justified (I/O, non-deterministic in tests)
  • You identify the file system mock as justified only if the handler writes to disk; otherwise unjustified
  • You identify the external email service mock as justified (network I/O, side effects)
  • You identify the logger mock as unjustified unless log output is being asserted — mocking it adds no value and may hide errors

P-F1SW02-3: Tracing a failure (Deterministic)

A test asserts expect(result.status).toBe(200) but observes 404. The handler is:

async function GET(req, { params }) {
  const { id } = await params;
  const record = await db.item.findUnique({ where: { id } });
  if (!record) return jsonError("Not found", 404);
  return jsonSuccess(record);
}

The test mocks db.item.findUnique to return { id: "abc", name: "test" }. The test passes id = "abc" in the request.

List the steps of the trace protocol applied to this failure. At which step does the failure localise, and what is the most likely cause?

Your answer is correct if:

  • You apply the trace in order (read assertion → nearest controllable point → observation → bisect → source)
  • You identify the most likely cause as the mock not being applied to the instance used by the handler (e.g., the mock targets a different import path or the module cache is not cleared between tests)
  • You do not suggest adding a fallback inside the handler to compensate for the mock failure

Reflective Task (Manual Scoring)

Describe a situation — real or plausible — where skipping tests for a single release led to a compounding problem in a later release. Explain what the first team that skipped the tests believed about the cost and what the actual cost turned out to be. Identify the point at which test-driven discipline would have changed the outcome, and what the discipline would have required at that point.

Evidence and source notes

This module is based on University of Claw institutional doctrine and course design. No external empirical sources are relied upon in this version.

Version history

Version Date Change
v0.1.0 2026-04-25 Initial publication.

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