The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Predicting Faults from Cached History
Abstract
We analyze the version history of 7 software systems to predict the most fault prone entities and files. The basic assumption is that faults do not occur in isolation, but rather in bursts of several related faults. Therefore, we cache locations that are likely to have faults: starting from the location of a known (fixed) fault, we cache the location itself, any locations changed together with the fault, recently added locations, and recently changed locations. By consulting the cache at the moment a fault is fixed, a developer can detect likely fault-prone locations. This is useful for prioritizing verification and validation resources on the most fault prone files or entities. In our evaluation of seven open source projects with more than 200,000 revisions, the cache selects 10% of the source code files; these files account for 73%-95% of faults - a significant advance beyond the state of the art.
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
Use of relative code churn measures to predict system defect density
Nagappan, N., & Ball, T. (2005) · Proceedings of the 27th international conference on Software engineering - ICSE '05 · doi:10.1145/1062455.1062514 · Cited by 775
Cited by
A Systematic Literature Review on Fault Prediction Performance in Software Engineering
Hall, T., Beecham, S., Bowes, D., Gray, D., & Counsell, S. (2012) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2011.103 · Cited by 1,154
Zhou, J., Zhang, H., & Lo, D. (2012) · 2012 34th International Conference on Software Engineering (ICSE) · doi:10.1109/icse.2012.6227210 · Cited by 507
Design Rule Spaces: A New Model for Representing and Analyzing Software Architecture
Cai, Y., Xiao, L., Kazman, R., Mo, R., & Feng, Q. (2019) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2018.2797899 · Cited by 39
Social engineering in social networking sites: Affect-based model
Algarni, A., Xu, Y., Chan, T., & Tian, Y.-C. (2013) · 8th International Conference for Internet Technology and Secured Transactions (ICITST-2013) · doi:10.1109/icitst.2013.6750253 · Cited by 41
Metadata
- Authors
- Kim, S., Zimmermann, T., Whitehead Jr., E. J., & Zeller, A.
- Venue
- 29th International Conference on Software Engineering (ICSE'07)
- Publisher
- IEEE
- Published
- 2007-05
- Pages
- 489-498
- ISSN
- 0270-5257 (ISSN-L)
- OpenAlex
- W2151553346
- Cited by
- 486
- References
- 1
- FWCI
- 15.51
- Type
- conference paper
- 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 Faults from Cached History. https://app.cochat.ai/papers/10.1109/icse.2007.66
- The paper
- Kim, S., Zimmermann, T., Whitehead Jr., E. J., & Zeller, A. (2007). Predicting Faults from Cached History. 29th International Conference on Software Engineering (ICSE'07). https://doi.org/10.1109/icse.2007.66
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.
