Comparing the Effectiveness of Software Testing Strategies
IEEE Transactions on Software Engineering
A semantic model of program faults
ISSTA '96 Proceedings of the 1996 ACM SIGSOFT international symposium on Software testing and analysis
An approach to fault modeling and fault seeding using the program dependence graph
Journal of Systems and Software
Incorporating varying test costs and fault severities into test case prioritization
ICSE '01 Proceedings of the 23rd International Conference on Software Engineering
Operational Profiles in Software-Reliability Engineering
IEEE Software
Toward A Quantifiable Definition of Software Faults
ISSRE '02 Proceedings of the 13th International Symposium on Software Reliability Engineering
Is mutation an appropriate tool for testing experiments?
Proceedings of the 27th international conference on Software engineering
Designing and comparing automated test oracles for GUI-based software applications
ACM Transactions on Software Engineering and Methodology (TOSEM)
Reliability of the Path Analysis Testing Strategy
IEEE Transactions on Software Engineering
Accounting for defect characteristics in evaluations of testing techniques
ACM Transactions on Software Engineering and Methodology (TOSEM)
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When choosing a testing technique, practitioners want to know which one will detect the faults that matter most to them in the programs that they plan to test. Do empirical evaluations of testing techniques provide this information? More often than not, they report how many faults in a carefully chosen "representative" sample the evaluated techniques detect. But the population of faults that such a sample would represent depends heavily on the faults' context or environment---as does the cost of failing to detect those faults. If empirical studies are to provide information that a practitioner can apply outside the context of the study, they must characterize the faults studied in a way that translates across contexts. A testing technique's fault-detecting abilities could then be interpreted relative to the fault characterization. In this paper, we present a list of criteria that a fault characterization must meet in order to be fit for this task, and we evaluate several well-known fault characterizations against the criteria. Two families of characterizations are found to satisfy the criteria: those based on graph models of programs and those based on faults' detection by testing techniques.