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Contribution Details
| Type | Conference or Workshop Paper |
| Scope | Discipline-based scholarship |
| Published in Proceedings | Yes |
| Title | Can Developer-Module Networks Predict Failures? |
| Organization Unit | |
| Authors |
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| Presentation Type | paper |
| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Page Range | 2 - 12 |
| Event Title | ACM SIGSOFT Symposium on the Foundations of Software Engineering |
| Event Type | conference |
| Event Location | Atlanta, Georgia, USA |
| Event Start Date | November 9 - 2008 |
| Event End Date | November 14 - 2008 |
| Abstract Text | Software teams should follow a well defined goal and keep their work focused. Work fragmentation is bad for efficiency and quality. In this paper we empirically investigate the relationship between the fragmentation of developer contributions and the number of post-release failures. Our approach is to represent developer contributions with a developer-module network that we call contribution network. We use network centrality measures to measure the degree of fragmentation of developer contributions. Fragmentation is determined by the centrality of software modules in the contribution network. Our claim is that central software modules are more likely to be failure-prone than modules located in surrounding areas of the network. We analyze this hypothesis by exploring the network centrality of Microsoft Windows Vista binaries using several network centrality measures as well as linear and logistic regression analysis. In particular, we investigate which centrality measures are significant to predict the probability and number of post-release failures. Results of our experiments show that central modules are more failure-prone than modules located in surrounding areas of the network. Results further confirm that number of authors and number of commits are significant predictors for the probability of post-release failures. For predicting the number of post-release failures the closeness centrality measure is most significant. |
| Official URL | https://cgi4.cc.gatech.edu/phps/conferences/fse16/ |
| Digital Object Identifier | 10.1145/1453101.1453105 |
| Other Identification Number | merlin-id:271 |
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