The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Predicting source code changes by mining change history
Abstract
Software developers are often faced with modification tasks that involve source which is spread across a code base. Some dependencies between source code, such as those between source code written in different languages, are difficult to determine using existing static and dynamic analyses. To augment existing analyses and to help developers identify relevant source code during a modification task, we have developed an approach that applies data mining techniques to determine change patterns - sets of files that were changed together frequently in the past - from the change history of the code base. Our hypothesis is that the change patterns can be used to recommend potentially relevant source code to a developer performing a modification task. We show that this approach can reveal valuable dependencies by applying the approach to the Eclipse and Mozilla open source projects and by evaluating the predictability and interestingness of the recommendations produced for actual modification tasks on these systems.
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Cites
Mining association rules between sets of items in large databases
Agrawal, R., Imieliński, T., & Swami, A. (1993) · Proceedings of the 1993 ACM SIGMOD international conference on Management of data · doi:10.1145/170035.170072 · Cited by 14,907
Mining frequent patterns without candidate generation
Han, J., Pei, J., & Yin, Y. (2000) · ACM SIGMOD Record · doi:10.1145/335191.335372 · Cited by 6,402
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Using information scent to model the dynamic foraging behavior of programmers in maintenance tasks
Lawrance, J., Bellamy, R., Burnett, M., & Rector, K. (2008) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/1357054.1357261 · Cited by 76
An Industrial Case Study of Program Artifacts Viewed During Maintenance Tasks
Zou, L., & Godfrey, M. W. (2006) · 2006 13th Working Conference on Reverse Engineering · doi:10.1109/wcre.2006.12 · Cited by 20
Towards Activity-Aware Tool Support for Change Tasks
Kevic, K., & Fritz, T. (2017) · 2017 IEEE International Conference on Software Maintenance and Evolution (ICSME) · doi:10.1109/icsme.2017.48 · Cited by 5
On the Relationship between Program Evolution and Fault-Proneness: An Empirical Study
Jaafar, F., Hassaine, S., Gueheneuc, Y., Hamel, S., & Adams, B. (2013) · 2013 17th European Conference on Software Maintenance and Reengineering · doi:10.1109/csmr.2013.12 · Cited by 5
Metadata
- Authors
- Ying, A. T. T., Murphy, G. C., Ng, R., & Chu-Carroll, M. C.
- Venue
- IEEE Transactions on Software Engineering
- Published
- 2004-09
- Volume
- 30
- Issue
- 9
- Pages
- 574-586
- ISSN
- 0098-5589 (ISSN-L)
- OpenAlex
- W2133961160
- Cited by
- 571
- References
- 2
- FWCI
- 96.59
- Type
- journal article
- Language
- en
- Open access
- closed
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- Ying, A. T. T., Murphy, G. C., Ng, R., & Chu-Carroll, M. C. (2004). Predicting source code changes by mining change history. IEEE Transactions on Software Engineering. https://doi.org/10.1109/tse.2004.52
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
