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ICST 2022
Mon 4 - Fri 8 April 2022
Mon 4 Apr 2022 14:00 - 14:20 at Marlyn Meltzer - Mutation I Chair(s): Donghwan Shin

Mutation testing aims to ensure that a test suite is capable of detecting real faults, by checking whether they can reveal (i.e., kill) small and arbitrary lexical changes made to the program (i.e., mutants). Some of these arbitrary changes may result in a mutant that is syntactically different but is semantically equivalent to the original program under test: such mutants are called equivalent mutants. Since program equivalence is undecidable in general, equivalent mutants pose a serious challenge to mutation testing. Given an unkilled mutant, it is not possible to automatically decide whether the cause is the weakness of test cases or the equivalence of the mutant. Recently machine learning has been adopted to train binary classification models for mutant equivalence. However, training such classification models requires a pool of equivalent mutants, the labelling for which involves a significant amount of human investigation. In this paper, we introduce two techniques that can be used to augment the equivalent mutant benchmarks. First, we propose a symbolic execution-based validation of mutant equivalence, instead of manual classification. Second, we introduce a synthesis technique for equivalent mutants: for a subset of mutation operators, the technique identifies potential mutation locations that are guaranteed to produce equivalent mutants. We compare these two techniques to MutantBench, a manually labelled equivalent mutant benchmark. For the 19 programs studied, MutantBench contains 462 equivalent mutants, whereas our technique is capable of generating 1,349 new and unique equivalent mutants automatically. We further show that the additional equivalent mutants can lead to more accurate equivalent mutant classification models.

Mon 4 Apr

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

14:00 - 15:00
Mutation IMutation at Marlyn Meltzer
Chair(s): Donghwan Shin University of Luxembourg
14:00
20m
Talk
Augmenting Equivalent Mutant Dataset Using Symbolic Execution
Mutation
14:20
20m
Talk
μBERT: Mutation Testing using Pre-Trained Language Models
Mutation
Renzo Degiovanni SnT, University of Luxembourg, Mike Papadakis University of Luxembourg, Luxembourg
14:40
20m
Talk
Random Mutant Selection and Equivalent Mutants Revisited
Mutation
Rowland Pitts George Mason University