Applying Social Network Analysis to Software Fault-Proneness Prediction

dc.contributor.VIAF65810187 (Wong, WE)
dc.contributor.advisorWong, W. Eric
dc.creatorLi, Yihao
dc.date.accessioned2017-09-03T20:08:06Z
dc.date.available2017-09-03T20:08:06Z
dc.date.created2017-08
dc.date.issued2017-08
dc.date.submittedAugust 2017
dc.date.updated2017-09-03T20:08:06Z
dc.description.abstractDue to the rapid pace of software development, end-users now anticipate a seemingly limitless expansion of capabilities from their software. As a result, software systems are becoming increasingly complex and more susceptible to failures. Although software fault localization techniques are becoming more comprehensive, it is still expensive to precisely locate, let alone fix, bugs in a program. Hence, fault-proneness prediction can be applied beforehand to alleviate the cost of program debugging by identifying software modules which are likely to contain faults. Meanwhile, social network analysis (SNA) has been frequently applied in software engineering to depict relations between (1) modules, (2) developers, or (3) modules and developers. Previous studies have shown that these relations have been used to build social networks to predict fault-prone modules and the results are encouraging. Although these networks are useful for fault-proneness prediction, they are built either by a single relation or by a pair of relations aforementioned. In addition, these networks appear to neglect an essential factor: developer quality. After all, it is developers who make mistakes and introduce faults into software. We therefore, propose Tri-Relation Network (TRN), a weighted social network that integrates all three types of relations. Four network node centrality metrics are correspondingly derived from TRN. Moreover, a calibration mechanism for edge weights on TRN is explored as well. Case studies reveal that TRN holds great promise in the context of fault-proneness prediction and the effectiveness improves further after applying the calibration mechanism on current TRN.
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/10735.1/5486
dc.language.isoen
dc.rightsCopyright ©2017 is held by the author. Digital access to this material is made possible by the Eugene McDermott Library. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
dc.subjectFault-tolerant computing
dc.subjectSoftware measurement
dc.subjectOnline social networks
dc.subjectDebugging in computer science
dc.subjectComputer software—Development
dc.titleApplying Social Network Analysis to Software Fault-Proneness Prediction
dc.typeDissertation
dc.type.materialtext
thesis.degree.departmentSoftware Engineering
thesis.degree.grantorUniversity of Texas at Dallas
thesis.degree.levelDoctoral
thesis.degree.namePHD

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