The DOI matches its Crossref record.
Towards identifying software project clusters with regard to defect prediction
Abstract
Background: This paper describes an analysis that was conducted on newly collected repository with 92 versions of 38 proprietary, open-source and academic projects. A preliminary study perfomed before showed the need for a further in-depth analysis in order to identify project clusters.
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
An 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
Software Defect Prediction via Convolutional Neural Network
Li, J., He, P., Zhu, J., & Lyu, M. R. (2017) · 2017 IEEE International Conference on Software Quality, Reliability and Security (QRS) · doi:10.1109/qrs.2017.42 · Cited by 462
Automated parameter optimization of classification techniques for defect prediction models
Tantithamthavorn, C., McIntosh, S., Hassan, A. E., & Matsumoto, K. (2016) · Proceedings of the 38th International Conference on Software Engineering · doi:10.1145/2884781.2884857 · Cited by 331
An empirical study on software defect prediction with a simplified metric set
He, P., Li, B., Liu, X., Chen, J., & Ma, Y. (2015) · Information and Software Technology · doi:10.1016/j.infsof.2014.11.006 · Cited by 309
Bennin, K. E., Keung, J., Phannachitta, P., Monden, A., & Mensah, S. (2018) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2017.2731766 · Cited by 298
Metadata
- Authors
- Jureczko, M., & Madeyski, L.
- Venue
- Proceedings of the 6th International Conference on Predictive Models in Software Engineering
- Publisher
- ACM
- Published
- 2010-09-12
- Pages
- 1-10
- OpenAlex
- W2025700486
- Cited by
- 546
- References
- 0
- FWCI
- 3.37
- 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: Towards identifying software project clusters with regard to defect prediction. https://app.cochat.ai/papers/10.1145/1868328.1868342
- The paper
- Jureczko, M., & Madeyski, L. (2010). Towards identifying software project clusters with regard to defect prediction. Proceedings of the 6th International Conference on Predictive Models in Software Engineering. https://doi.org/10.1145/1868328.1868342
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.
