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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchA passing test shows that, in that particular run and under its specific setup, the observed result matched the expectation the test encoded. It does not prove the expectation was correct, that important user risks were covered, or that the test would catch the defect you care about. A useful green result is evidence with a defined scope—not a certificate that the software is correct.
What a passing test establishes
Start with the test’s actual claim: a particular scenario was executed, and its observed result satisfied an encoded expectation. As Sri Ramya puts it, a pass “proves that the test reached the expected result for that particular scenario” (DEV Community, September 28, 2026).
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The claim is bounded by the test’s code, inputs, setup, dependencies, and the version of the software exercised. If the test expects the wrong thing, omits a relevant condition, or checks only a weak proxy for the desired behavior, it can pass while the behavior that matters is broken. In plain terms: green means the test did not object in that run; it does not tell you whether the test could object to the failure you care about.
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Code coverage records whether specified parts of a program’s structure were exercised. The ISTQB syllabus describes structural coverage as the extent to which structural elements—such as executable statements or decision outcomes—were exercised, expressed as a percentage of that element type (ISTQB CTFL Syllabus 2018 v3.1.1, released July 1, 2021).
A test can execute a line and still fail to check whether the line produced the right result. For example, a test might call a function and assert only that it returned a non-null value, even though the important requirement is that it calculated the correct total. Execution is evidence that the code ran; the assertion determines what outcome the test actually verified.
How to interpret a coverage percentage
Coverage is useful as a map of what tests did and did not exercise, not as a standalone grade for test quality. A low number can point to unexecuted code that deserves investigation. A high number cannot show on its own that assertions are strong, that tests represent critical workflows, or that the tested expectations match requirements. Martin Fowler summarizes the distinction in “Test Coverage”: “Test coverage is of little use as a numeric statement of how good your tests are.”
Do not treat 100% coverage as proof of correctness or adopt a universal threshold as a substitute for judgment. Interpret the metric alongside what the tests assert and which risks they address.
Ask what would make the test fail
For a test that supports an important decision, inspect the assertions rather than relying on the test name or the green badge. State its claim in one sentence, then consider a plausible defect that could exist without making the test fail. This turns a pass into a more useful question: what kinds of mistakes would this test detect, and what could it miss?
- Read the assertion. Identify the exact result or behavior it checks, and compare that with the requirement the test is meant to protect.
- Inspect the setup. Check whether the inputs, user state, fixtures, mocks, and dependency behavior represent the conditions relevant to the risk.
- Consider a plausible defect. Ask whether an incorrect boundary, calculation, state transition, or business rule could still satisfy the assertion.
- Compare the test claim with the risk. If the test protects a critical workflow, verify that its scenario and expected result address the failure that would matter to users or the business.
Use mutation testing to probe detection
Mutation testing makes the failure question more concrete. A tool such as PIT introduces small changes to code and reruns the tests. A mutation is “killed” when a test detects the change; it “survives” when the relevant tests do not catch it. A surviving mutation is a clue that the suite may not verify the behavior affected by that change.
Mutation results are diagnostic, not a proof of product correctness. Some mutations may be equivalent in behavior, invalid, or affected by test-run errors, which complicates interpretation. The technique samples artificial changes; it cannot establish that every important real-world defect would be detected.
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Build confidence from several kinds of evidence
Confidence is stronger when test results are considered against requirements, realistic conditions, and the risks the software must control. When reviewing a test suite or comparing approaches, use these questions together rather than relying on test count or one coverage figure:
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- Do the scenarios include important user states, data boundaries, and decision paths?
- Do the assertions check the required behavior, rather than merely execution or a weak proxy?
- Do fixtures, mocks, and dependencies preserve the conditions that could expose the relevant failure?
- Are results stable enough to serve as reliable evidence?
- Do deliberate code changes reveal behaviors the tests fail to detect?
This is not a single score or formal standard. It is a way to make the scope of a green result explicit: what was exercised, what was checked, and which risks remain outside that evidence.
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