The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
A comparative analysis of the efficiency of change metrics and static code attributes for defect prediction
Abstract
In this paper we present a comparative analysis of the predictive power of two different sets of metrics for defect prediction. We choose one set of product related and one set of process related software metrics and use them for classifying Java files of the Eclipse project as defective respective defect-free. Classification models are built using three common machine learners: logistic regression, Naïve Bayes, and decision trees. To allow different costs for prediction errors we perform cost-sensitive classification, which proves to be very successful: >75% percentage of correctly classified files, a recall of >80%, and a false positive rate <30%. Results indicate that for the Eclipse data, process metrics are more efficient defect predictors than code metrics.
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
A systematic review of machine learning techniques for software fault prediction
Malhotra, R. (2015) · Applied Soft Computing · doi:10.1016/j.asoc.2014.11.023 · Cited by 620
Revisiting the Impact of Classification Techniques on the Performance of Defect Prediction Models
Ghotra, B., McIntosh, S., & Hassan, A. E. (2015) · 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering · doi:10.1109/icse.2015.91 · Cited by 364
A survey of AIOps methods for failure management
Notaro, P., Cardoso, J., & Gerndt, M. (2021) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3483424 · Cited by 142
Examining the Impact of Self-Admitted Technical Debt on Software Quality
Wehaibi, S., Shihab, E., & Guerrouj, L. (2016) · 2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER) · doi:10.1109/saner.2016.72 · Cited by 114
Metadata
- Authors
- Moser, R., Pedrycz, W., & Succi, G.
- Venue
- Proceedings of the 13th international conference on Software engineering - ICSE '08
- Publisher
- ACM Press
- Published
- 2008
- Pages
- 181
- ISSN
- 0270-5257 (ISSN-L)
- OpenAlex
- W2132887549
- Cited by
- 701
- References
- 1
- FWCI
- 19.41
- 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: A comparative analysis of the efficiency of change metrics and static code attributes for defect prediction. https://app.cochat.ai/papers/10.1145/1368088.1368114
- The paper
- Moser, R., Pedrycz, W., & Succi, G. (2008). A comparative analysis of the efficiency of change metrics and static code attributes for defect prediction. Proceedings of the 13th international conference on Software engineering - ICSE '08. https://doi.org/10.1145/1368088.1368114
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.
