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Can Developers' Interaction Data Improve Change Recommendation?
KTH, School of Computer Science and Communication (CSC). Tokyo Institute of Technology, Japan.
2017 (English)In: 2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC), IEEE Computer Society, 2017, p. 128-137, article id 8029600Conference paper, Published paper (Refereed)
Abstract [en]

One of the most common causes of bugs is overlooking changes. To prevent bugs and improve the quality of the products, numerous studies have been undertaken on change guides based on logical couplings extracted from developers' past process histories, such as change history. While valuable change rules based on logical couplings can be gleaned found from the change history, these rules often fail to find appropriate candidates because the change histories in repositories only preserve a summary of changes between commits. We recently analyzed the interaction data produced by a developer in an integrated development environment. Such interaction data contains not only a detailed change history but also reference activities between commits. In this paper, we investigate whether logical couplings extracted from interaction data could improve change recommendation performance. We used the interaction data from actual open source development, not from the project only for this study. Experimental results obtained using the interaction data from actual open source development showed a significant improvement in the efficiency of the change recommendation process. The results also indicated improvement in the number of detected artifacts that the developer had forgot to change.

Place, publisher, year, edition, pages
IEEE Computer Society, 2017. p. 128-137, article id 8029600
Series
Proceedings - International Computer Software and Applications Conference, ISSN 0730-3157
Keywords [en]
Change Guide, Change Impact Analysis, Interaction Data, Mining Software Repository, Software Maintenance
National Category
Other Engineering and Technologies not elsewhere specified
Identifiers
URN: urn:nbn:se:kth:diva-223026DOI: 10.1109/COMPSAC.2017.79ISI: 000424861400017Scopus ID: 2-s2.0-85029840337ISBN: 9781538603673 OAI: oai:DiVA.org:kth-223026DiVA, id: diva2:1182703
Conference
41st IEEE Annual Computer Software and Applications Conference, COMPSAC 2017, Torino, Italy, 4 July 2017 through 8 July 2017
Note

QC 20180214

Available from: 2018-02-14 Created: 2018-02-14 Last updated: 2018-03-12Bibliographically approved

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Hagward, Anders Mikael

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Citation style
  • apa
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