Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-20869
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dc.contributor.authorPanichella, Annibale-
dc.contributor.authorPanichella, Sebastiano-
dc.contributor.authorFraser, Gordon-
dc.contributor.authorSawant, Anand Ashok-
dc.contributor.authorHellendoorn, Vincent J.-
dc.date.accessioned2020-11-19T10:56:15Z-
dc.date.available2020-11-19T10:56:15Z-
dc.date.issued2020-
dc.identifier.isbn978-1-7281-5619-4de_CH
dc.identifier.isbn978-1-7281-5620-0de_CH
dc.identifier.issn2576-3148de_CH
dc.identifier.issn1063-6773de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/20869-
dc.description​© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.de_CH
dc.description.abstractTest smells attempt to capture design issues in test code that reduce their maintainability. Previous work found such smells to be highly common in automatically generated test-cases, but based this result on specific static detection rules; although these are based on the original definition of "test smells", a recent empirical study showed that developers perceive these as overly strict and non-representative of the maintainability and quality of test suites. This leads us to investigate how effective such test smell detection tools are on automatically generated test suites. In this paper, we build a dataset of 2,340 test cases automatically generated by EVOSUITE for 100 Java classes. We performed a multi-stage, cross-validated manual analysis to identify six types of test smells and label their instances. We benchmark the performance of two test smell detection tools: one widely used in prior work, and one recently introduced with the express goal to match developer perceptions of test smells. Our results show that these test smell detection strategies poorly characterized the issues in automatically generated test suites; the older tool’s detection strategies, especially, misclassified over 70% of test smells, both missing real instances (false negatives) and marking many smell-free tests as smelly (false positives). We identify common patterns in these tests that can be used to improve the tools, refine and update the definition of certain test smells, and highlight as of yet uncharacterized issues. Our findings suggest the need for (i) more appropriate metrics to match development practice; and (ii) more accurate detection strategies, to be evaluated primarily in industrial contexts.de_CH
dc.language.isoende_CH
dc.publisherIEEEde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subject.ddc005: Computerprogrammierung, Programme und Datende_CH
dc.titleRevisiting test smells in automatically generated tests : limitations, pitfalls, and opportunitiesde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Informatik (InIT)de_CH
dc.identifier.doi10.1109/ICSME46990.2020.00056de_CH
dc.identifier.doi10.21256/zhaw-20869-
zhaw.conference.detailsInternational Conference on Software Maintenance (ICSM), Adelaide, Australia, 28 September - 2 October 2020de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end533de_CH
zhaw.pages.start523de_CH
zhaw.publication.statusacceptedVersionde_CH
zhaw.publication.reviewNot specifiedde_CH
zhaw.title.proceedings2020 IEEE International Conference on Software Maintenance and Evolution (ICSME)de_CH
zhaw.webfeedSoftware Systemsde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Panichella, A., Panichella, S., Fraser, G., Sawant, A. A., & Hellendoorn, V. J. (2020). Revisiting test smells in automatically generated tests : limitations, pitfalls, and opportunities [Conference paper]. 2020 IEEE International Conference on Software Maintenance and Evolution (ICSME), 523–533. https://doi.org/10.1109/ICSME46990.2020.00056
Panichella, A. et al. (2020) ‘Revisiting test smells in automatically generated tests : limitations, pitfalls, and opportunities’, in 2020 IEEE International Conference on Software Maintenance and Evolution (ICSME). IEEE, pp. 523–533. Available at: https://doi.org/10.1109/ICSME46990.2020.00056.
A. Panichella, S. Panichella, G. Fraser, A. A. Sawant, and V. J. Hellendoorn, “Revisiting test smells in automatically generated tests : limitations, pitfalls, and opportunities,” in 2020 IEEE International Conference on Software Maintenance and Evolution (ICSME), 2020, pp. 523–533. doi: 10.1109/ICSME46990.2020.00056.
PANICHELLA, Annibale, Sebastiano PANICHELLA, Gordon FRASER, Anand Ashok SAWANT und Vincent J. HELLENDOORN, 2020. Revisiting test smells in automatically generated tests : limitations, pitfalls, and opportunities. In: 2020 IEEE International Conference on Software Maintenance and Evolution (ICSME). Conference paper. IEEE. 2020. S. 523–533. ISBN 978-1-7281-5619-4
Panichella, Annibale, Sebastiano Panichella, Gordon Fraser, Anand Ashok Sawant, and Vincent J. Hellendoorn. 2020. “Revisiting Test Smells in Automatically Generated Tests : Limitations, Pitfalls, and Opportunities.” Conference paper. In 2020 IEEE International Conference on Software Maintenance and Evolution (ICSME), 523–33. IEEE. https://doi.org/10.1109/ICSME46990.2020.00056.
Panichella, Annibale, et al. “Revisiting Test Smells in Automatically Generated Tests : Limitations, Pitfalls, and Opportunities.” 2020 IEEE International Conference on Software Maintenance and Evolution (ICSME), IEEE, 2020, pp. 523–33, https://doi.org/10.1109/ICSME46990.2020.00056.


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