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dc.contributor.authorLin, Y
dc.contributor.authorSun, J
dc.contributor.authorFraser, G
dc.contributor.authorXiu, Z
dc.contributor.authorLiu, T
dc.contributor.authorDong, JS
dc.date.accessioned2020-11-18T03:00:37Z
dc.date.available2020-11-18T03:00:37Z
dc.date.issued2020
dc.identifier.isbn9781450380089
dc.identifier.doi10.1145/3395363.3397358
dc.identifier.urihttp://hdl.handle.net/10072/399419
dc.description.abstractIn Search-based Software Testing (SBST), test generation is guided by fitness functions that estimate how close a test case is to reach an uncovered test goal (e.g., branch). A popular fitness function estimates how close conditional statements are to evaluating to true or false, i.e., the branch distance. However, when conditions read Boolean variables (e.g., if(x && y)), the branch distance provides no gradient for the search, since a Boolean can either be true or false. This flag problem can be addressed by transforming individual procedures such that Boolean flags are replaced with numeric comparisons that provide better guidance for the search. Unfortunately, defining a semantics-preserving transformation that is applicable in an interprocedural case, where Boolean flags are passed around as parameters and return values, is a daunting task. Thus, it is not yet supported by modern test generators. This work is based on the insight that fitness gradients can be recovered by using runtime information: Given an uncovered interprocedural flag branch, our approach (1) calculates context-sensitive branch distance for all control flows potentially returning the required flag in the called method, and (2) recursively aggregates these distances into a continuous value. We implemented our approach on top of the EvoSuite framework for Java, and empirically compared it with state-of-the-art testability transformations on non-trivial methods suffering from interprocedural flag problems, sampled from open source Java projects. Our experiment demonstrates that our approach achieves higher coverage on the subject methods with statistical significance and acceptable runtime overheads.
dc.description.peerreviewedYes
dc.publisherAssociation for Computing Machinery (ACM)
dc.relation.ispartofconferencenameInternational Symposiumon Software Testing and Analysis (ISSTA 2020)
dc.relation.ispartofconferencetitleISSTA 2020 - Proceedings of the 29th ACM SIGSOFT International Symposium on Software Testing and Analysis
dc.relation.ispartofdatefrom2020-07-18
dc.relation.ispartofdateto2020-07-22
dc.relation.ispartoflocationNew York, USA
dc.relation.ispartofpagefrom440
dc.relation.ispartofpageto451
dc.subject.fieldofresearchSoftware engineering
dc.subject.fieldofresearchcode4612
dc.titleRecovering fitness gradients for interprocedural Boolean flags in search-based testing
dc.typeConference output
dc.type.descriptionE1 - Conferences
dcterms.bibliographicCitationLin, Y; Sun, J; Fraser, G; Xiu, Z; Liu, T; Dong, JS, Recovering fitness gradients for interprocedural Boolean flags in search-based testing, ISSTA 2020 - Proceedings of the 29th ACM SIGSOFT International Symposium on Software Testing and Analysis, 2020, pp. 440-451
dc.date.updated2020-11-18T02:59:07Z
gro.hasfulltextNo Full Text
gro.griffith.authorDong, Jin-Song


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