| Title | Efficient mutation analysis by propagating and partitioning infected execution states |
| Publication Type | Conference Paper |
| Year of Publication | 2014 |
| Authors | Just R, Ernst MD, Fraser G |
| Conference Name | ISSTA 2014, Proceedings of the 2014 International Symposium on Software Testing and Analysis |
| Pagination | 315–326 |
| Date or Month Published | July |
| Conference Location | San Jose, CA, USA |
| Abstract | Mutation analysis evaluates a testing technique by measuring how well it detects seeded faults (mutants). Mutation analysis is hampered by inherent scalability problems –- a test suite is executed for each of a large number of mutants. Despite numerous optimizations presented in the literature, this scalability issue remains, and this is one of the reasons why mutation analysis is hardly used in practice. \par Whereas most previous optimizations attempted to statically reduce the number of executions or their computational overhead, this paper exploits information available only at run time to further reduce the number of executions. \par First, \emphstate infection conditions can reveal –- with a single test execution of the unmutated program –- which mutants would lead to a different state, thus avoiding unnecessary test executions. Second, determining whether an infected execution state \emphpropagates can further reduce the number of executions. Mutants that are embedded in compound expressions may infect the state locally without affecting the outcome of the compound expression. Third, those mutants that do infect the state can be \emphpartitioned based on the resulting infected state –- if two mutants lead to the same infected state, only one needs to be executed as the result of the other can be inferred. \par We have implemented these optimizations in the Major mutation framework and empirically evaluated them on 14 open source programs. The optimizations basefilename = "state-infection-issta2014 |
| Downloads | https://mutation-testing.org/ Major mutation framework and experimental data
https://homes.cs.washington.edu/~mernst/pubs/state-infection-issta2014.pdf PDF
https://homes.cs.washington.edu/~mernst/pubs/state-infection-issta2014-s... slides (PDF)
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| Citation Key | JustEF2014 |
