While all options highlight potential test gaps, the most significant and fundamental issue is that a single test case cannot adequately represent an equivalence partition. The test only covers one valid combination. Effective unit testing requires partitioning inputs into groups (e.g., valid discounts 1-99%, boundary 0% and 100%, invalid like >100% or negative) to ensure the logic works for all representative values, not just one example.

The developer is identifying input conditions (valid credentials, wrong password, non-existent user) that should be treated equivalently by the system. By selecting one representative test value from each of these distinct partitions, they are applying the Equivalence Partitioning technique to reduce the number of tests while still effectively evaluating the behavior for each type of input.

The efficient strategy combines positive and negative testing. For each of the three valid equivalence partitions (formats), one representative valid date confirms the basic functionality. More importantly, testing several invalid dates (e.g., ’32/13/2023′, ’12-34-ABCD’, using wrong delimiters) is crucial to verify the function correctly rejects malformed input and handles errors gracefully, which is a key objective of unit testing.

The main drawback is a lack of test independence. A single test with multiple assertions has a single reason to fail: verifying the state of the returned list. However, if it fails, the developer must debug to find which specific assertion failed (Was it null? The count? The property?). Best practice is to structure this as three separate, focused tests (one for non-null, one for count, one for property). This provides immediate, clear feedback on the exact nature of any defect, aligning with the ‘Single Assertion Per Test’ guideline for clarity and maintainability.

The core purpose of a unit test is to verify the unit of code (ConfigLoader) in isolation. The real FileSystem introduces external dependencies, potential slowness, required file setup, and side effects. Using a test double (e.g., a stub or mock) allows the tester to control the test environment completely, simulate various file-reading scenarios (success, empty file, missing file), and focus solely on whether the ConfigLoader’s logic responds correctly to those controlled inputs. This defines isolation, a cornerstone of unit testing.

The developer’s concern is valid but points to the need for different test levels. Unit tests aim to verify the *unit’s logic* in isolation. Using a real database moves the test towards integration testing. The correct approach is to use mocks in unit tests to verify the application code handles both successful and failed dependency responses (e.g., a mocked exception). The actual correctness of the SQL and the integration with the real database is a separate concern, properly validated by integration tests, which are a different, complementary part of the test strategy.

While the test has multiple assertions and could be structured better, its most fundamental flaw is that the test cases (2,2), (3,7), etc., appear arbitrary. Good unit tests should be traceable to a requirement or specification (e.g., ‘The function shall return the mathematical sum of two integers’). The chosen values should systematically verify that requirement through techniques like equivalence partitioning (positive, negative, zero) and boundary value analysis, not just random correct arithmetic.

The TDD cycle is Red-Green-Refactor. 1. Red: Write a failing test. 2. Green: Write the *minimal* amount of production code required to make that test pass, even if the implementation seems naive or incomplete. This keeps the focus small and ensures the code is always driven by tests. 3. Refactor: Clean up the code, including the test code, while ensuring all tests still pass. Writing more tests first (option 2) or refactoring (option 0) are steps that come later in the cycle.

The most effective and efficient tests target the boundaries of the equivalence partitions. The partitions are: ‘Should NOT log’ (DEBUG, INFO) and ‘Should log’ (WARN, ERROR). By testing ‘INFO’ (the highest value in the ‘no-log’ partition) and ‘WARN’ (the lowest value in the ‘log’ partition), you test the two most likely points for an ‘off-by-one’ error in the filtering logic. This is Boundary Value Analysis applied to ordered equivalence partitions and provides strong confidence with fewer tests than testing every single value.

While all options highlight potential test gaps, the most significant and fundamental issue is that a single test case cannot adequately represent an equivalence partition. The test only covers one valid combination. Effective unit testing requires partitioning inputs into groups (e.g., valid discounts 1-99%, boundary 0% and 100%, invalid like >100% or negative) to ensure the logic works for all representative values, not just one example.0