The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Identifying reasons for software changes using historic databases
Abstract
Large scale software products must constantly change in order to adapt to a changing environment. Studies of historic data from legacy software systems have identified three specific causes of this change: adding new features; correcting faults; and restructuring code to accommodate future changes. Our hypothesis is that a textual description field of a change is essential to understanding why that change was performed. Also, we expect that difficulty, size, and interval would vary strongly across different types of changes. To test these hypotheses we have designed a program which automatically classifies maintenance activity based on a textual description of changes. Developer surveys showed that the automatic classification was in agreement with developer opinions. Tests of the classifier on a different product found that size and interval for different types of changes did not vary across two products. We have found strong relationships between the type and size of a change and the time required to carry it out. We also discovered a relatively large amount of perfective changes in the system we examined. From this study we have arrived at several suggestions on how to make version control data useful in diagnosing the state of a software project, without significantly increasing the overhead for the developer using the change management system.
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McCullagh, P., & Nelder, J. A. (1989) · doi:10.1007/978-1-4899-3242-6 · Cited by 16,836
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PaReco: patched clones and missed patches among the divergent variants of a software family
Ramkisoen, P. K., Businge, J., van Bladel, B., Decan, A., Demeyer, S., De Roover, C., & Khomh, F. (2022) · Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering · doi:10.1145/3540250.3549112 · Cited by 10
Entropy Guided Spectrum Based Bug Localization Using Statistical Language Model
Chakraborty, S., Li, Y., Irvine, M., Saha, R., & Ray, B. (2018) · arXiv · doi:10.48550/arxiv.1802.06947 · Cited by 7
“First, we identified bug-fix commits by searching a project’s commit logs using bug-fix related keywords: ‘error’, ‘bug’, ‘fix’, ‘is-sue’, ‘mistake’, ‘flaw’, and ‘defect’, following the methodology described by Mockus et al. [41].”
Metadata
- Authors
- Mockus, & Votta
- Venue
- Proceedings 2000 International Conference on Software Maintenance
- Publisher
- IEEE
- Published
- 2000
- Pages
- 120-130
- OpenAlex
- W2164519300
- Cited by
- 472
- References
- 1
- FWCI
- 4.82
- Type
- conference paper
- Language
- en
- Open access
- closed
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