Software defect prediction using global and local models
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s13198-024-02407-7.pdf
Reference36 articles.
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2. Amasaki S (2017) On applicability of cross-project defect prediction method for multi-versions projects. In: Proceedings of the 13th international conference on predictive models and data analytics in software engineering—PROMISE, ACM Press, New York, USA, pp 93–96. https://doi.org/10.1145/3127005.3127015.
3. Arasteh B (2018) Software fault-prediction using combination of neural network and naive bayes algorithm. J Netw Technol 9(3):94. https://doi.org/10.6025/jnt/2018/9/3/94-101
4. Arasteh B, Sadegi R, Arasteh K (2021) Bölen: software module clustering method using the combination of shuffled frog leaping and genetic algorithm. DTA 55(2):251–279. https://doi.org/10.1108/DTA-08-2019-0138
5. Bettenburg N, Nagappan M, Hassan AE (2015) Towards improving statistical modeling of software engineering data: think locally, act globally! Empir Softw Eng 20(2):294–335. https://doi.org/10.1007/s10664-013-9292-6
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