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A passing test run shows that the tests which ran did not report a failure for the code, inputs, environment and checks used in that run. It does not prove the software is correct or free of defects. If all your tests pass, the useful question is: Would they fail if a plausible bug were present?
What a green test run actually establishes
A green suite is evidence about a bounded set of conditions. Its result depends on four things:
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- Which tests ran: a test omitted from the command, skipped by configuration or absent from the suite cannot reveal a defect.
- Which inputs and states they exercised: untested values, sequences, error paths and environmental conditions remain unexamined.
- What environment they ran in: different operating systems, dependencies, data or runtime conditions may expose different behavior.
- What each test checked: a test needs an expectation—an assertion or other oracle—that can distinguish correct behavior from incorrect behavior.
The International Software Testing Qualifications Board (ISTQB) states: “Testing can show that defects are present, but cannot prove that there are no defects.” Its Certified Tester Foundation Level syllabus, version 2018 v3.1.1, was released on 1 July 2021. ISTQB Certified Tester Foundation Level
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Did the tests reach the behavior that matters?
Before interpreting a green result, trace the path from a test to the behavior it is meant to protect. A test can execute successfully while missing the relevant function, taking a different branch or never checking the result that matters.
In a 19 September 2026 DEV Community article, Hamber describes writing four tests for an audio-glitch fix in GoGBA. The author reports that all four passed, but none exercised the function that configured the fix; two also passed after the fix was disabled. This is an author-reported example, not an independently verified project test. Its practical lesson is to check that a test reaches the intended behavior and would fail if that behavior were removed. Hamber’s GoGBA testing account
A useful review question is: What plausible change or defect would make this test fail? If there is no clear answer, the test may not be checking the behavior you think it is.
Coverage shows execution, not whether a test would catch a bug
Execution coverage can show which lines, branches or other code units ran during a test run. It cannot, by itself, establish that the tests checked the right outcomes. A line can execute while its result is ignored, or while an assertion accepts both the correct and an incorrect result.
Google’s “Code Coverage Best Practices” guidance distinguishes coverage from the quality of the checks: coverage can help identify unexercised code, but it is not a stand-alone measure of whether tests detect defects. Use it to find areas worth investigating, not as a universal quality score. Google Testing Blog: Code Coverage Best Practices
Mutation testing probes whether tests notice changes
Mutation testing makes small, deliberate changes to code and checks whether the test suite detects them. Goran Petrovic describes it as “a method of evaluating test quality by injecting bugs into the code and seeing whether the tests detect the fault or not.” Google Testing Blog: Mutation Testing
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A mutation that causes a relevant test to fail is often called “killed”; one that leaves the tests green “survives.” A surviving mutant can point to a missing or weak check, but it is not automatically proof of a bad test: some changes are equivalent in effect, or do not represent a useful defect. Review the mutation and the behavior it represents rather than treating a score as a verdict.
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Petrovic’s 12 April 2021 account reports an experiment examining mutants related to historical bug fixes. Under Google’s described setup and mutation-filtering heuristics, a bug was coupled with a mutation in around 70% of cases. The same report says that in more than 90% of cases, either all mutants on a line were killed or none were; the experiment executed 33 million test suites. These are results from that specific experiment, not predictions of what a mutation-testing run will find in another codebase. The report also discusses limitations, including equivalent or unproductive mutants.
Best Value
Use complementary checks for different questions
No single testing measure answers every question. Choose checks based on the uncertainty you want to reduce:
| Approach | What it can reveal | Cost and limits |
|---|---|---|
| Execution coverage | Which code ran during the selected tests. | Usually useful as a diagnostic, but execution alone does not show that expected behavior was asserted. |
| Mutation testing | Whether tests detect selected small code changes. | Can require substantial execution and review; equivalent or unproductive mutants add noise. |
| Integration or acceptance tests | Whether connected components or user-visible workflows behave as expected under the scenarios tested. | Can reveal failures hidden by isolated tests, but covers only its chosen workflows and conditions. |
| Requirements and invariant review | Whether test expectations correspond to intended behavior and important constraints. | Depends on clear requirements and careful review; it does not replace running tests against the implementation. |
These methods expose different kinds of risk. Combining them can provide more useful evidence than maximizing one metric, but none turns a finite test suite into a proof that no defect exists.
Make passing results more informative
Derive assertions from intended behavior
Write expected outcomes from requirements, user-visible behavior or explicit invariants—not by simply copying what the current implementation does. Otherwise, a test can faithfully confirm existing behavior without establishing that the behavior is the one the product needs.
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Choose edge cases by risk
Because exhaustive testing is usually infeasible, prioritize cases where failure would matter or where the behavior is especially uncertain. Depending on the feature, that may include boundary values, invalid input, empty data, error handling, state transitions or interactions with another component. These are prompts for risk-based selection, not a checklist that every feature must implement identically.
Quick Recap
Verify that tests exercise and check the claimed behavior
- Trace a test through the code path it is intended to cover.
- Confirm that it asserts an outcome relevant to the requirement, rather than only that execution completed.
- For critical behavior, consider whether removing or changing that behavior would make the test fail; mutation testing can help probe this.
- Investigate skipped tests, untested branches and surviving mutants in context instead of treating any one count as a complete quality measure.
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