A compositional model for cross-project effort-aware defect prediction

Lin Zhu; Yanjiao Zhang; Shuang Yin; Yanhui Li

doi:10.1117/12.3009530

16 October 2023 A compositional model for cross-project effort-aware defect prediction

Lin Zhu, Yanjiao Zhang, Shuang Yin, Yanhui Li

Proceedings Volume 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023); 128031F (2023) https://doi.org/10.1117/12.3009530
Event: 2023 5th International Conference on Artificial Intelligence and Computer Science (AICS 2023), 2023, Wuhan, China

Abstract

The Cross-Project Effort-Aware Defect Prediction (CPEADP) model can effectively use detection resources and the data from different projects to build models. One factor affecting the performance of CPEADP is the problem of data distribution differences in cross-project settings. The classification performance also greatly impacts the predictive capabilities of the models. Therefore, we propose the BDA-DF model and conduct experiments on 11 cross-project datasets from the PROMISE repository. Compared to traditional data filtering and transfer learning methods, our approach exhibits significant improvements across five effort-aware metrics, including Precision@20%, Recall@20%, F1@20%, PofB@20%, and IFA. To explore the optimal classifier for CPEADP, we embed seven different classifiers into BDA. The experimental results on the BDA embedded with different classifiers reveal that DF exhibits the best overall performance.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Lin Zhu, Yanjiao Zhang, Shuang Yin, and Yanhui Li "A compositional model for cross-project effort-aware defect prediction", Proc. SPIE 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023), 128031F (16 October 2023); https://doi.org/10.1117/12.3009530

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