Selecting Software Test Data Using Data Flow Information
IEEE Transactions on Software Engineering
Comparing the Effectiveness of Software Testing Strategies
IEEE Transactions on Software Engineering
An Applicable Family of Data Flow Testing Criteria
IEEE Transactions on Software Engineering
Software testing techniques (2nd ed.)
Software testing techniques (2nd ed.)
A study of the effectiveness of control and data flow testing strategies
Journal of Systems and Software - Special issue on applying specification, verification, and validation techniques to industrial software systems
How many paths are needed for branch testing?
Journal of Systems and Software - Special issue on software reliability issues
Coverage measurement experience during function test
ICSE '93 Proceedings of the 15th international conference on Software Engineering
Experiments of the effectiveness of dataflow- and controlflow-based test adequacy criteria
ICSE '94 Proceedings of the 16th international conference on Software engineering
All-uses vs mutation testing: an experimental comparison of effectiveness
Journal of Systems and Software
Simulation Modeling Using @Risk
Simulation Modeling Using @Risk
A Comparison of Some Structural Testing Strategies
IEEE Transactions on Software Engineering
An Experimental Comparison of the Effectiveness of Branch Testing and Data Flow Testing
IEEE Transactions on Software Engineering
More Experience with Data Flow Testing
IEEE Transactions on Software Engineering
Provable Improvements on Branch Testing
IEEE Transactions on Software Engineering
Residual fault density prediction using regression methods
ISSRE '96 Proceedings of the The Seventh International Symposium on Software Reliability Engineering
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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 shows 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 this is no general answer to the problem above, we propose a procedure 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 is applicable in typical testing conditions where the software is tested once, according to a given strategy, and where coverage measures are collected as well as defect data. We then test the procedure on published data and compare our results 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.