The untold impact of learning approaches on software fault-proneness predictions: an analysis of temporal aspects
Author:
Funder
National Science Foundation
NASA Software Assurance Research Program
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10664-024-10454-8.pdf
Reference97 articles.
1. Agrawal A, Menzies T (2018) Is “Better Data” Better Than “Better Data Miners”? In: International conference on software engineering, pp 1050–1061. https://doi.org/10.1145/3180155.3180197, 1705.03697
2. Ahmad MJ, Goseva-Popstojanova K, Lutz RR (2022) Online supplemental document for the untold impact of learning approaches on software fault-proneness predictions. https://tinyurl.com/UntolImpact
3. Ahmad MJ (2021) Analysis and classification of software fault-proneness and vulnerabilities. PhD thesis, West Virginia University. https://researchrepository.wvu.edu/etd/8323
4. Alshehri YA, Goseva-Popstojanova K, Dzielski DG, Devine T (2018) Applying machine learning to predict software fault proneness using change metrics, static code metrics, and a combination of them. In: IEEE Southeastcon, pp 1–7. https://doi.org/10.1109/SECON.2018.8478911
5. Amasaki S (2020) Cross-version defect prediction: use historical data, cross-project data, or both? Empir Softw Eng 25(2):1573–1595. https://doi.org/10.1007/s10664-019-09777-8
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