Reference : Watch out for This Commit! A Study of Influential Software Changes
Reports : Internal report
Engineering, computing & technology : Computer science
Computational Sciences
http://hdl.handle.net/10993/27742
Watch out for This Commit! A Study of Influential Software Changes
English
Li, Daoyuan mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Li, Li mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Kim, Dongsun mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Bissyande, Tegawendé François D Assise mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Lo, David mailto []
Le Traon, Yves mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Jun-2016
SnT
12
978-2-87971-153-9
TR-SnT-2016-6
[en] One single code change can significantly influence a wide range of software systems and their users. For example, 1) adding a new feature can spread defects in several modules, while 2) changing an API method can improve the performance of all client programs. Developers often may not clearly know whether their or others’ changes are influential at commit time. Rather, it turns out to be influential after affecting many aspects of a system later.
This paper investigates influential software changes and proposes an approach to identify them early, i.e., immediately when they are applied. We first conduct a post- mortem analysis to discover existing influential changes by using intuitions such as isolated changes and changes referred by other changes in 10 open source projects. Then we re-categorize all identified changes through an open-card sorting process. Subsequently, we conduct a survey with 89 developers to confirm our influential change categories. Finally, from our ground truth we extract features, including metrics such as the complexity of changes, terms in commit logs and file centrality in co-change graphs, to build ma- chine learning classifiers. The experiment results show that our prediction model achieves overall with random samples 86.8% precision, 74% recall and 80.4% F-measure respectively.
Researchers ; Professionals ; Students ; General public
http://hdl.handle.net/10993/27742
FnR ; FNR10449467 > Tegawende Francois D'Assise Bissyande > RECOMMEND > Automatic Bug Fix Recommendation: Improving Software Repair and Reducing Time-to-Fix Delays in Software Development Projects > 01/11/2015 > 31/01/2019 > 2015

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