With the increasing popularity of continuous integration, algorithms for
selecting the minimal test-suite to cover a given set of changes are in order.
This paper reports on how polymorphism can handle false negatives in a previous
algorithm which uses method-level changes in the base-code to deduce which
tests need to be rerun. We compare the approach with and without polymorphism
on two distinct cases ---PMD and CruiseControl--- and discovered an interesting
trade-off: incorporating polymorphism results in more relevant tests to be
included in the test suite (hence improves accuracy), however comes at the cost
of a larger test suite (hence increases the time to run the minimal
test-suite).Comment: The final publication is available at link.springer.co
Abstract. Mutation testing is a well-studied method for increasing the quality of a test suite. We designed LittleDarwin as a mutation testing framework able to cope with large and complex Java software systems, while still being easily extensible with new experimental components. LittleDarwin addresses two existing problems in the domain of mutation testing: having a tool able to work within an industrial setting, and yet, be open to extension for cutting edge techniques provided by academia. LittleDarwin already offers higher-order mutation, null type mutants, mutant sampling, manual mutation, and mutant subsumption analysis. There is no tool today available with all these features that is able to work with typical industrial software systems.
Mutation testing is a standard technique to evaluate the quality of a test suite. Due to its computationally intensive nature, many approaches have been proposed to make this technique feasible in real case scenarios. Among these approaches, uniform random mutant selection has been demonstrated to be simple and promising. However, works on this area analyze mutant samples at project level mainly on projects with adequate test suites. In this paper, we fill this lack of empirical validation by analyzing random mutant selection at class level on projects with non-adequate test suites. First, we show that uniform random mutant selection underachieves the expected results. Then, we propose a new approach named weighted random mutant selection which generates more representative mutant samples. Finally, we show that representative mutant samples are larger for projects with high test adequacy.
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