The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Can developer-module networks predict failures?
Open accessAbstract
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.
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Cites
Social Network Analysis: Methods and Applications
Wasserman, S., & Faust, K. (1994) · doi:10.1017/cbo9780511815478 · Cited by 17,557
Centrality in social networks conceptual clarification
Freeman, L. C. (1978) · Social Networks · doi:10.1016/0378-8733(78)90021-7 · Cited by 17,062
Use of relative code churn measures to predict system defect density
Nagappan, N., & Ball, T. (2005) · Proceedings of the 27th international conference on Software engineering - ICSE '05 · doi:10.1145/1062455.1062514 · Cited by 775
Self-citation
Cited by
A Systematic Literature Review on Fault Prediction Performance in Software Engineering
Hall, T., Beecham, S., Bowes, D., Gray, D., & Counsell, S. (2012) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2011.103 · Cited by 1,154
Shameer, S., Rodríguez-Pérez, G., & Nagappan, M. (2023) · Empirical Software Engineering · doi:10.1007/s10664-023-10313-y · Cited by 13
When Agents Coordinate: Measuring Coordination in Multi-Agent AI Coding
Destefanis, G., & Aste, T. (2026) · arXiv · doi:10.48550/arxiv.2608.16801 · Cited by 3
Metadata
- Authors
- Pinzger, M., Nagappan, N., & Murphy, B.
- Venue
- Proceedings of the 16th ACM SIGSOFT International Symposium on Foundations of software engineering
- Publisher
- ACM
- Published
- 2008-11-09
- Pages
- 2-12
- OpenAlex
- W1988510359
- Cited by
- 256
- References
- 3
- FWCI
- 12.07
- Type
- conference paper
- Language
- en
- Open access
- green
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- Pinzger, M., Nagappan, N., & Murphy, B. (2008). Can developer-module networks predict failures? Proceedings of the 16th ACM SIGSOFT International Symposium on Foundations of software engineering. https://doi.org/10.1145/1453101.1453105
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