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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-22413
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dc.contributor.authorRoy, Devjeet-
dc.contributor.authorZhang, Ziyi-
dc.contributor.authorMa, Maggie-
dc.contributor.authorArnaoudova, Venera-
dc.contributor.authorPanichella, Annibale-
dc.contributor.authorPanichella, Sebastiano-
dc.contributor.authorGonzalez, Danielle-
dc.contributor.authorMirakhorli, Mehdi-
dc.date.accessioned2021-05-05T12:11:35Z-
dc.date.available2021-05-05T12:11:35Z-
dc.date.issued2020-
dc.identifier.isbn978-1-4503-6768-4de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/22413-
dc.description.abstractAutomated test case generation tools have been successfully proposed to reduce the amount of human and infrastructure resources required to write and run test cases. However, recent studies demonstrate that the readability of generated tests is very limited due to (i) uninformative identifiers and (ii) lack of proper documentation. Prior studies proposed techniques to improve test readability by either generating natural language summaries or meaningful methods names. While these approaches are shown to improve test readability, they are also affected by two limitations: (1) generated summaries are often perceived as too verbose and redundant by developers, and (2) readable tests require both proper method names but also meaningful identifiers (within-method readability). In this work, we combine template based methods and Deep Learning (DL) approaches to automatically generate test case scenarios (elicited from natural language patterns of test case statements) as well as to train DL models on path-based representations of source code to generate meaningful identifier names. Our approach, called DeepTC-Enhancer, recommends documentation and identifier names with the ultimate goal of enhancing readability of automatically generated test cases. An empirical evaluation with 36 external and internal developers shows that (1) DeepTC-Enhancer outperforms significantly the baseline approach for generating summaries and performs equally with the baseline approach for test case renaming, (2) the transformation proposed by DeepTC-Enhancer results in a significant increase in readability of automatically generated test cases, and (3) there is a significant difference in the feature preferences between external and internal developers.de_CH
dc.language.isoende_CH
dc.publisherAssociation for Computing Machineryde_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectSoftware testingde_CH
dc.subjectDeep learningde_CH
dc.subjectTest case generationde_CH
dc.subjectProgram comprehensionde_CH
dc.subjectEmpirical studyde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleDeepTC-Enhancer : improving the readability of automatically generated testsde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Informatik (InIT)de_CH
dc.identifier.doi10.1145/3324884.3416622de_CH
dc.identifier.doi10.21256/zhaw-22413-
zhaw.conference.details35th IEEE/ACM International Conference on Automated Software Engineering (ASE), Virtual Event, 21-25 September 2020de_CH
zhaw.funding.euinfo:eu-repo/grantAgreement/EC/H2020/957254//DevOps for Complex Cyber-physical Systems/COSMOSde_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end298de_CH
zhaw.pages.start287de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewNot specifiedde_CH
zhaw.title.proceedingsProceedings of the 35th IEEE/ACM International Conference on Automated Software Engineeringde_CH
zhaw.webfeedSoftware Systemsde_CH
zhaw.funding.zhawCOSMOS – DevOps for Complex Cyber-physical Systems of Systemsde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Roy, D., Zhang, Z., Ma, M., Arnaoudova, V., Panichella, A., Panichella, S., Gonzalez, D., & Mirakhorli, M. (2020). DeepTC-Enhancer : improving the readability of automatically generated tests [Conference paper]. Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering, 287–298. https://doi.org/10.1145/3324884.3416622
Roy, D. et al. (2020) ‘DeepTC-Enhancer : improving the readability of automatically generated tests’, in Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering. Association for Computing Machinery, pp. 287–298. Available at: https://doi.org/10.1145/3324884.3416622.
D. Roy et al., “DeepTC-Enhancer : improving the readability of automatically generated tests,” in Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering, 2020, pp. 287–298. doi: 10.1145/3324884.3416622.
ROY, Devjeet, Ziyi ZHANG, Maggie MA, Venera ARNAOUDOVA, Annibale PANICHELLA, Sebastiano PANICHELLA, Danielle GONZALEZ und Mehdi MIRAKHORLI, 2020. DeepTC-Enhancer : improving the readability of automatically generated tests. In: Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering. Conference paper. Association for Computing Machinery. 2020. S. 287–298. ISBN 978-1-4503-6768-4
Roy, Devjeet, Ziyi Zhang, Maggie Ma, Venera Arnaoudova, Annibale Panichella, Sebastiano Panichella, Danielle Gonzalez, and Mehdi Mirakhorli. 2020. “DeepTC-Enhancer : Improving the Readability of Automatically Generated Tests.” Conference paper. In Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering, 287–98. Association for Computing Machinery. https://doi.org/10.1145/3324884.3416622.
Roy, Devjeet, et al. “DeepTC-Enhancer : Improving the Readability of Automatically Generated Tests.” Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering, Association for Computing Machinery, 2020, pp. 287–98, https://doi.org/10.1145/3324884.3416622.


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