MBL-CPDP: A Multi-objective Bilevel Method for Cross-Project Defect Prediction via Automated Machine Learning

• URL: http://arxiv.org/abs/2411.06491v1
• Date: Sun, 10 Nov 2024 15:17:15 GMT
• Title: MBL-CPDP: A Multi-objective Bilevel Method for Cross-Project Defect Prediction via Automated Machine Learning
• Authors: Jiaxin Chen, Jinliang Ding, Kay Chen Tan, Jiancheng Qian, Ke Li,
• Abstract summary: Cross-project defect prediction (CPDP) leverages machine learning (ML) techniques to proactively identify software defects, especially where project-specific data is scarce. This paper formulates CPDP as a multi-objective bilevel optimization (MBLO) method, dubbed MBL-CPDP. It comprises two nested problems: the upper-level, a multi-objective optimization problem, and the lower-level problem, an expensive optimization problem.
• Score: 34.89241736003651
• License:
• Abstract: Cross-project defect prediction (CPDP) leverages machine learning (ML) techniques to proactively identify software defects, especially where project-specific data is scarce. However, developing a robust ML pipeline with optimal hyperparameters that effectively use cross-project information and yield satisfactory performance remains challenging. In this paper, we resolve this bottleneck by formulating CPDP as a multi-objective bilevel optimization (MBLO) method, dubbed MBL-CPDP. It comprises two nested problems: the upper-level, a multi-objective combinatorial optimization problem, enhances robustness and efficiency in optimizing ML pipelines, while the lower-level problem is an expensive optimization problem that focuses on tuning their optimal hyperparameters. Due to the high-dimensional search space characterized by feature redundancy and inconsistent data distributions, the upper-level problem combines feature selection, transfer learning, and classification to leverage limited and heterogeneous historical data. Meanwhile, an ensemble learning method is proposed to capture differences in cross-project distribution and generalize across diverse datasets. Finally, a MBLO algorithm is presented to solve this problem while achieving high adaptability effectively. To evaluate the performance of MBL-CPDP, we compare it with five automated ML tools and $50$ CPDP techniques across $20$ projects. Extensive empirical results show that MBL-CPDPoutperforms the comparison methods, demonstrating its superior adaptability and comprehensive performance evaluation capability.

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