Grid Search-Optimized Artificial Neural Network for Heterogeneous Cross-Project Defect Prediction
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-6550-2_34
Reference25 articles.
1. Tong H, Wei L, Weiwei X, Wang S (2023) ARRAY: adaptive triple feature-weighted transfer Naive Bayes for cross-project defect prediction. J Syst Softw 202:111721. https://doi.org/10.1016/j.jss.2023.111721
2. How far does the predictive decision impact the software project? The cost, service time, and failure analysis from a cross-project defect prediction model. J Syst Softw 195:111522. https://doi.org/10.1016/j.jss.2022.111522
3. Seyedrebvar H, Burak T, Gunarathna D (2017) A systematic literature review and meta-analysis on cross project defect prediction. IEEE Trans Softw Eng 45:111–147
4. Liu Y, Khoshgoftaar TM, Seliya N (2010) Evolutionary optimization of software quality modeling with multiple repositories. IEEE Trans Softw Eng 36:852–864. https://doi.org/10.1109/TSE.2010.51
5. Zong X, Li G, Zheng S, Zou H, Yu H, Gao S (2023) Heterogeneous cross-project defect prediction via optimal transport. IEEE Access 11:12015–12030. https://doi.org/10.1109/ACCESS.2023.3241924
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