Improving Transfer Learning for Cross Project Defect Prediction

Abstract

—Cross-project defect prediction (CPDP) makes use of cross-project (CP) data to overcome the lack of data necessary to train well-performing software defect prediction (SDP) classifiers in the early stage of new software projects. Since the CP data (known as the source) may be different from the new project’s data (known as the target), this makes it difficult for CPDP classifiers to perform well. In particular, it is a mismatch of data distributions between source and target that creates this difficulty. Transfer learning-based CPDP classifiers are designed to minimize these distribution differences. The first Transfer learning-based CPDP classifiers treated these differences equally, thereby degrading prediction performance. To this end, recent research has proposed the Weighted Balanced Distribution Adaptation (W-BDA) method to leverage the importance of both distribution differences to improve classification performance. Although W-BDA has been shown to improve model performance in CPDP, research to date has failed to consider model performance in light of increasing target data or variances in data sampling. We provide the first investigation of when and to what extent the effect of increasing the target data and using various sampling techniques have when leveraging the importance of both distribution differences. We extend the initial W-BDA method and call this extension the W-BDA<sup>+</sup>‘ method. To evaluate the effectiveness of W-BDA<sup>+</sup>‘ for improving CPDP performance, we conduct eight experiments on 18 projects from four datasets where data sampling was performed with different sampling methods. We evaluate our method using four complementary indicators (i.e., Balanced Accuracy, AUC, F-measure and G-Measure). Our findings reveal an average improvement of 6%, 7.5%, 10% and 12% for these four indicators when W-BDA<sup>+</sup>‘ is compared to five other baseline methods (including W-BDA), for all four of the sampling methods used. Also, as the target to source ratio is increased with different sampling methods, we observe a decrease in performance for the original W-BDA, with our W-BDA<sup>+</sup> approach outperforming the original W-BDA in most cases. Our results highlight the importance of adjusting for data imbalance and having an awareness of the effect of the increasing availability of target data in CPDP scenarios.

Revisiting ‘revisiting supervised methods for effort‐aware cross‐project defect prediction’

“…Xu et al [68] applied the Balanced Distribution Adaption (BDA) method to CPDP, which not only considered the marginal and conditional distributions, but also assigned different weights to them, adaptively. Subsequently, Omondiagerbe et al [85] proposed the Weighted-BDA + method to improve CPDP performance, which incorporated the ratio of within-project and cross-project modules into the BDA model learning process and adjusted the weights of the marginal and conditional distributions. Wu et al [81] proposed a cost-sensitive kernelised semi-supervised dictionary learning method for CPDP, which can use limited labelled defective modules and lots of unlabelled modules in the kernel space and consider the misclassification cost during dictionary learning.…”