The DOI matches its Crossref record.
Toward understanding the rhetoric of small source code changes
Abstract
Understanding the impact of software changes has been a challenge since software systems were first developed. With the increasing size and complexity of systems, this problem has become more difficult. There are many ways to identify the impact of changes on the system from the plethora of software artifacts produced during development, maintenance, and evolution. We present the analysis of the software development process using change and defect history data. Specifically, we address the problem of small changes by focusing on the properties of the changes rather than the properties of the code itself. Our study reveals that 1) there is less than 4 percent probability that a one-line change introduces a fault in the code, 2) nearly 10 percent of all changes made during the maintenance of the software under consideration were one-line changes, 3) nearly 50 percent of the changes were small changes, 4) nearly 40 percent of changes to fix faults resulted in further faults, 5) the phenomena of change differs for additions, deletions, and modifications as well as for the number of lines affected, and 6) deletions of up to 10 lines did not cause faults.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Change Distilling:Tree Differencing for Fine-Grained Source Code Change Extraction
Fluri, B., Wursch, M., PInzger, M., & Gall, H. (2007) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2007.70731 · Cited by 607
Deep Learning for Just-in-Time Defect Prediction
Yang, X., Lo, D., Xia, X., Zhang, Y., & Sun, J. (2015) · 2015 IEEE International Conference on Software Quality, Reliability and Security · doi:10.1109/qrs.2015.14 · Cited by 352
Automatically documenting program changes
Buse, R. P. L., & Weimer, W. R. (2010) · Proceedings of the IEEE/ACM international conference on Automated software engineering · doi:10.1145/1858996.1859005 · Cited by 219
CCT5: A Code-Change-Oriented Pre-trained Model
Lin, B., Wang, S., Liu, Z., Liu, Y., Xia, X., & Mao, X. (2023) · Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering · doi:10.1145/3611643.3616339 · Cited by 69
Metadata
- Authors
- Purushothaman, R., & Perry, D. E.
- Venue
- IEEE Transactions on Software Engineering
- Published
- 2005-06
- Volume
- 31
- Issue
- 6
- Pages
- 511-526
- ISSN
- 0098-5589 (ISSN-L)
- OpenAlex
- W2120703352
- Cited by
- 216
- References
- 0
- FWCI
- 30.95
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Toward understanding the rhetoric of small source code changes. https://app.cochat.ai/papers/10.1109/tse.2005.74
- The paper
- Purushothaman, R., & Perry, D. E. (2005). Toward understanding the rhetoric of small source code changes. IEEE Transactions on Software Engineering. https://doi.org/10.1109/tse.2005.74
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.