The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Predicting failures with developer networks and social network analysis
Abstract
Software fails and fixing it is expensive. Research in failure prediction has been highly successful at modeling software failures. Few models, however, consider the key cause of failures in software: people. Understanding the structure of developer collaboration could explain a lot about the reliability of the final product. We examine this collaboration structure with the developer network derived from code churn information that can predict failures at the file level. We conducted a case study involving a mature Nortel networking product of over three million lines of code. Failure prediction models were developed using test and post-release failure data from two releases, then validated against a subsequent release. One model's prioritization revealed 58% of the failures in 20% of the files compared with the optimal prioritization that would have found 61% in 20% of the files, indicating that a significant correlation exists between file-based developer network metrics and failures.
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Cites
Situated Learning: Legitimate Peripheral Participation
Lave, J., & Wenger, E. (1991) · Cambridge University Press eBooks · doi:10.1017/cbo9780511815355 · Cited by 35,324
Network biology: understanding the cell's functional organization
Barabási, A.-L., & Oltvai, Z. N. (2004) · Nature Reviews Genetics · doi:10.1038/nrg1272 · Cited by 7,903
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
Cited by
Automatically learning semantic features for defect prediction
Wang, S., Liu, T., & Tan, L. (2016) · Proceedings of the 38th International Conference on Software Engineering · doi:10.1145/2884781.2884804 · Cited by 689
An empirical study on software defect prediction with a simplified metric set
He, P., Li, B., Liu, X., Chen, J., & Ma, Y. (2015) · Information and Software Technology · doi:10.1016/j.infsof.2014.11.006 · Cited by 309
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
- Meneely, A., Williams, L., Snipes, W., & Osborne, J.
- Venue
- Proceedings of the 16th ACM SIGSOFT International Symposium on Foundations of software engineering
- Publisher
- ACM
- Published
- 2008-11-09
- Pages
- 13-23
- OpenAlex
- W2107672258
- Cited by
- 245
- References
- 3
- FWCI
- 10.75
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Meneely, A., Williams, L., Snipes, W., & Osborne, J. (2008). Predicting failures with developer networks and social network analysis. Proceedings of the 16th ACM SIGSOFT International Symposium on Foundations of software engineering. https://doi.org/10.1145/1453101.1453106
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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