Cross-Project Defect Prediction via Transferable Deep Learning-Generated and Handcrafted Features
Shaojian Qiu, Lu Lu, Ziyi Cai, Siyu Jiang · Proceedings/Proceedings of the ... International Conference on Software Engineering and Knowledge Engineering · 2019
Although the machine learning-based software defect prediction (SDP) method has shown promising value in software engineering, yet challenges remain.To improve the performance of SDP, some researchers have used deep learning algorithms to extract the semantic and structural features of the program.However, in more practical cross-project defect prediction (CPDP) tasks, whether deep learning-generated features can be directly used should be explored due to the data distribution shift that usually exists in different projects.In this paper, we propose a Transferable Hybrid Features Learning with Convolutional Neural Network (CNN-THFL) framework to conduct CPDP.Specially, CNN-THFL mines deep learning-generated features from token vectors extracted from programs' abstract syntax trees via convolutional neural network.Furthermore, CNN-THFL learns the transferable joint features simultaneously considering deep learning-generated and handcrafted features by applying a transfer component analysis algorithm.Finally, the features generated by CNN-THFL are fed to the classifier to train a defect prediction model.Extensive experiments verify that CNN-THFL can outperform referential methods on 72 pairs of CPDP tasks formed by 9 open-source projects.