The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Predicting risk of software changes
Abstract
Reducing the number of software failures is one of the most challenging problems of software production. We assume that software development proceeds as a series of changes and model the probability that a change to software will cause a failure. We use predictors based on the properties of a change itself. Such predictors include size in lines of code added, deleted, and unmodified; diffusion of the change and its component subchanges, as reflected in the number of files, modules, and subsystems touched, or changed; several measures of developer experience; and the type of change and its subchanges (fault fixes or new code). The model is built on historic information and is used to predict the risk of new changes. In this paper we apply the model to 5ESS® software updates and find that change diffusion and developer experience are essential to predicting failures. The predictive model is implemented as a Web-based tool to allow timely prediction of change quality. The ability to predict the quality of change enables us to make appropriate decisions regarding inspection, testing, and delivery. Historic information on software changes is recorded in many commercial software projects, suggesting that our results can be easily and widely applied in practice.
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
Cites
McCullagh, P., & Nelder, J. A. (1989) · doi:10.1007/978-1-4899-3242-6 · Cited by 16,836
Cited by
An empirical study of speed and communication in globally distributed software development
Herbsleb, J. D., & Mockus, A. (2003) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2003.1205177 · Cited by 997
Self-citationAn 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
Predicting Faults from Cached History
Kim, S., Zimmermann, T., Whitehead Jr., E. J., & Zeller, A. (2007) · 29th International Conference on Software Engineering (ICSE'07) · doi:10.1109/icse.2007.66 · Cited by 486
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
Don't touch my code!: examining the effects of ownership on software quality
Bird, C., Nagappan, N., Murphy, B., Gall, H., & Devanbu, P. (2011) · Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering · doi:10.1145/2025113.2025119 · Cited by 340
Metadata
- Authors
- Mockus, A., & Weiss, D. M.
- Venue
- Bell Labs Technical Journal
- Published
- 2002-08-28
- Volume
- 5
- Issue
- 2
- Pages
- 169-180
- ISSN
- 1089-7089 (ISSN-L)
- OpenAlex
- W2007705030
- Cited by
- 429
- References
- 1
- FWCI
- 21.94
- 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: Predicting risk of software changes. https://app.cochat.ai/papers/10.1002/bltj.2229
- The paper
- Mockus, A., & Weiss, D. M. (2002). Predicting risk of software changes. Bell Labs Technical Journal. https://doi.org/10.1002/bltj.2229
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.
