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We investigate claims and assumptions made in several recent papers about fault localization (FL) techniques. Most of these claims have to do with evaluating FL accuracy. Our investigation centers on a new subject program having properties useful for FL experiments. We find that Tarantula (Jones et al.) works well on the program, and we show weak support for the assertion that coverage-based test...
Mutation is the practice of automatically generating possibly faulty variants of a program, for the purpose of assessing the adequacy of a test suite or comparing testing techniques. The cost of mutation often makes its application infeasible. The cost of mutation is usually assessed in terms of the number of mutants, and consequently the number of "mutation operators" that produce them...
Test result evaluation programs often take the form of log file analyzers, which analyze text logs of events that have happened during testing. Previously, we proposed a methodology for deriving logging instrumentation and state-based log file analyzer programs from requirements. In this paper, we report on an industrial evaluation in which the methodology was carried out on two pieces of commercial...
The empirical assessment of test techniques plays an important role in software testing research. One common practice is to seed faults in subject software, either manually or by using a program that generates all possible mutants based on a set of mutation operators. The latter allows the systematic, repeatable seeding of large numbers of faults, thus facilitating the statistical analysis of fault...
We describe a framework for randomized unit testing, and give empirical evidence that generating unit test cases randomly and then minimizing the failing test cases results in significant benefits. Randomized generation of unit test cases (sequences of method calls) has been shown to allow high coverage and to be highly effective. However, failing test cases, if found, are often very long sequences...
We study coverage-checked random unit testing (CRUT), the practice of repeatedly testing units on sequences of random function calls until given code coverage goals are achieved. Previous research has shown that this practice can be a useful complement to traditional testing methods. However, questions remained as to the breadth of its applicability. In this paper, we report on a case study in which...
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