Abstract—The use of test-coverage measures (e.g., block-coverage)
to control the software test process has become an
increasingly common practice. This is justified by the assumption
that higher test-coverage helps achieve higher defect-coverage
and therefore improves software quality. In practice, data often
show that defect-coverage and test-coverage grow over time, as
additional testing is performed. However, it is unclear whether this
phenomenon of concurrent growth can be attributed to a causal
dependency, or if it is coincidental, simply due to the cumulative
nature of both measures. Answering such a question is important
as it determines whether a given test-coverage measure should be
monitored for quality control and used to drive testing.
Although it is no general answer to this problem, a procedure is
proposed to investigate whether any test-coverage criterion has a
genuine additional impact on defect-coverage when compared to
the impact of just running additional test cases. This procedure
applies in typical testing conditions where
• the software is tested once, according to a given strategy,
• coverage measures are collected as well as defect data.
This procedure is tested on published data, and the results are compared
with the original findings. The study outcomes do not support
the assumption of a causal dependency between test-coverage
and defect-coverage, a result for which several plausible explanations
are provided.
Index Terms—Defect-coverage, Monte Carlo simulation, Software
test, Test-coverage, Test intensity.
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