1. ZHAW digitalcollection
  2. School of Engineering
  3. Publikationen
  4. Publikationen School of Engineering
Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-3221
Full metadata record
DC FieldValueLanguage
dc.contributor.authorGrano, Giovanni-
dc.contributor.authorTitov, Timofey V.-
dc.contributor.authorPanichella, Sebastiano-
dc.contributor.authorGall, Harald C.-
dc.date.accessioned2019-06-19T13:05:10Z-
dc.date.available2019-06-19T13:05:10Z-
dc.date.issued2019-
dc.identifier.issn2047-7473de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/17315-
dc.descriptionThis is the peer reviewed version which has been published in final form at [DOI]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.de_CH
dc.description.abstractSoftware testing is crucial in continuous integration (CI). Ideally, at every commit, all the test cases should be executed, and moreover, new test cases should be generated for the new source code. This is especially true in a Continuous Test Generation (CTG) environment, where the automatic generation of test cases is integrated into the continuous integration pipeline. In this context, developers want to achieve a certain minimum level of coverage for every software build. However, executing all the test cases and, moreover, generating new ones for all the classes at every commit is not feasible. As a consequence, developers have to select which subset of classes has to be tested and/or targeted by test‐case generation. We argue that knowing a priori the branch coverage that can be achieved with test‐data generation tools can help developers into taking informed decision about those issues. In this paper, we investigate the possibility to use source‐code metrics to predict the coverage achieved by test‐data generation tools. We use four different categories of source‐code features and assess the prediction on a large data set involving more than 3'000 Java classes. We compare different machine learning algorithms and conduct a fine‐grained feature analysis aimed at investigating the factors that most impact the prediction accuracy. Moreover, we extend our investigation to four different search budgets. Our evaluation shows that the best model achieves an average 0.15 and 0.21 MAE on nested cross‐validation over the different budgets, respectively, on EVOSUITE and RANDOOP. Finally, the discussion of the results demonstrate the relevance of coupling‐related features for the prediction accuracy.de_CH
dc.language.isoende_CH
dc.publisherWileyde_CH
dc.relation.ispartofJournal of Software: Evolution and Processde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subject.ddc005: Computerprogrammierung, Programme und Datende_CH
dc.titleBranch coverage prediction in automated testingde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Informatik (InIT)de_CH
dc.identifier.doi10.21256/zhaw-3221-
dc.identifier.doi10.1002/smr.2158de_CH
zhaw.funding.euNode_CH
zhaw.issue9de_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.starte2158de_CH
zhaw.publication.statusacceptedVersionde_CH
zhaw.volume31de_CH
zhaw.embargo.end2020-04-01de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.funding.snf166275de_CH
zhaw.webfeedService Engineeringde_CH
Appears in collections:Publikationen School of Engineering

Files in This Item:
File Description SizeFormat 
jsep.pdfAccepted Version690.67 kBAdobe PDFThumbnail
View/Open
Show simple item record
Grano, G., Titov, T. V., Panichella, S., & Gall, H. C. (2019). Branch coverage prediction in automated testing. Journal of Software: Evolution and Process, 31(9), e2158. https://doi.org/10.21256/zhaw-3221
Grano, G. et al. (2019) ‘Branch coverage prediction in automated testing’, Journal of Software: Evolution and Process, 31(9), p. e2158. Available at: https://doi.org/10.21256/zhaw-3221.
G. Grano, T. V. Titov, S. Panichella, and H. C. Gall, “Branch coverage prediction in automated testing,” Journal of Software: Evolution and Process, vol. 31, no. 9, p. e2158, 2019, doi: 10.21256/zhaw-3221.
GRANO, Giovanni, Timofey V. TITOV, Sebastiano PANICHELLA und Harald C. GALL, 2019. Branch coverage prediction in automated testing. Journal of Software: Evolution and Process. 2019. Bd. 31, Nr. 9, S. e2158. DOI 10.21256/zhaw-3221
Grano, Giovanni, Timofey V. Titov, Sebastiano Panichella, and Harald C. Gall. 2019. “Branch Coverage Prediction in Automated Testing.” Journal of Software: Evolution and Process 31 (9): e2158. https://doi.org/10.21256/zhaw-3221.
Grano, Giovanni, et al. “Branch Coverage Prediction in Automated Testing.” Journal of Software: Evolution and Process, vol. 31, no. 9, 2019, p. e2158, https://doi.org/10.21256/zhaw-3221.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.