The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Predicting defects using network analysis on dependency graphs
Abstract
In software development, resources for quality assurance are limited by time and by cost. In order to allocate resources effectively, managers need to rely on their experience backed by code complexity metrics. But often dependencies exist between various pieces of code over which managers may have little knowledge. These dependencies can be construed as a low level graph of the entire system. In this paper, we propose to use network analysis on these dependency graphs. This allows managers to identify central program units that are more likely to face defects. In our evaluation on Windows Server 2003, we found that the recall for models built from network measures is by 10% points higher than for models built from complexity metrics. In addition, network measures could identify 60% of the binaries that the Windows developers considered as critical-twice as many as identified by complexity metrics.
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Cites
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An Empirical Comparison of Model Validation Techniques for Defect Prediction Models
Tantithamthavorn, C., McIntosh, S., Hassan, A. E., & Matsumoto, K. (2017) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2016.2584050 · Cited by 574
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Self-citationIdentifying linked incidents in large-scale online service systems
Chen, Y., Yang, X., Dong, H., He, X., Zhang, H., Lin, Q., Chen, J., Zhao, P., Kang, Y., Gao, F., et al. (2020) · Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering · doi:10.1145/3368089.3409768 · Cited by 47
Metadata
- Authors
- Zimmermann, T., & Nagappan, N.
- Venue
- Proceedings of the 13th international conference on Software engineering - ICSE '08
- Publisher
- ACM Press
- Published
- 2008
- Pages
- 531
- ISSN
- 0270-5257 (ISSN-L)
- OpenAlex
- W2135198476
- Cited by
- 580
- References
- 38
- FWCI
- 28.59
- Type
- conference paper
- Language
- en
- Open access
- closed
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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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