The DOI matches its Crossref record.
Predicting fault-proneness using OO metrics. An industrial case study
Abstract
Software quality is an important external software attribute that is difficult to measure objectively. In this case study, we empirically validate a set of object-oriented metrics in terms of their usefulness in predicting fault-proneness, an important software quality indicator We use a set of ten software product metrics that relate to the following software attributes: the size of the software, coupling, cohesion, inheritance, and reuse. Eight hypotheses on the correlations of the metrics with fault-proneness are given. These hypotheses are empirically tested in a case study, in which the client side of a large network service management system is studied. The subject system is written in Java and it consists of 123 classes. The validation is carried out using two data analysis techniques: regression analysis and discriminant analysis.
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
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
Software fault prediction metrics: A systematic literature review
Radjenović, D., Heričko, M., Torkar, R., & Živkovič, A. (2013) · Information and Software Technology · doi:10.1016/j.infsof.2013.02.009 · Cited by 538
Jabangwe, R., Börstler, J., Šmite, D., & Wohlin, C. (2015) · Empirical Software Engineering · doi:10.1007/s10664-013-9291-7 · Cited by 97
Metadata
- Authors
- Yu, P., Systa, T., & Muller, H.
- Venue
- Proceedings of the Sixth European Conference on Software Maintenance and Reengineering
- Publisher
- IEEE Comput. Soc
- Published
- 2003-06-25
- Pages
- 99-107
- ISSN
- 1534-5351 (ISSN-L)
- OpenAlex
- W2156292929
- Cited by
- 164
- References
- 0
- FWCI
- 8.31
- 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 fault-proneness using OO metrics. An industrial case study. https://app.cochat.ai/papers/10.1109/csmr.2002.995794
- The paper
- Yu, P., Systa, T., & Muller, H. (2003). Predicting fault-proneness using OO metrics. An industrial case study. Proceedings of the Sixth European Conference on Software Maintenance and Reengineering. https://doi.org/10.1109/csmr.2002.995794
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.
