Cross-project Defect Prediction via ASTToken2Vec and BLSTM-based Neural Network

Hao Li, Xiaohong Li, Xiang Chen, Xiaofei Xie, Yanzhou Mu, Zhiyong Feng

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

10 被引用数 (Scopus)

抄録

Cross-project defect prediction (CPDP) as a means to focus quality assurance of software projects was under heavy investigation in recent years. In this paper, we propose a novel CPDP approach via deep learning. In particular, we model each program module via simplified abstract syntax tree (S-AST). For each node in S-AST, only the project-independent node type is remained and other project-specific information (such as name of variable and method) is ignored, so that the modeling method is project-independent and suitable for CPDP issue. Then we extract token sequences from program modules modeled as S-AST. In addition, to construct meaningful vector representations for token sequences, we propose a novel unsupervised embedding method ASTToken2Vec, which learns semantic information from S-AST's natural structure. Finally, we use BLSTM (bi-directional long short-term memory) based neural network to automatically learn semantic features from vectorized token sequences and construct CPDP models. In our empirical studies, 10 real large-scale open source Java projects are chosen as our empirical subjects. Final results show that our proposed CPDP approach can perform significantly better than 5 state-of-the-art CPDP baselines in terms of AUC.

本文言語英語
ホスト出版物のタイトル2019 International Joint Conference on Neural Networks, IJCNN 2019
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781728119854
DOI
出版ステータス出版済み - 7月 2019
外部発表はい
イベント2019 International Joint Conference on Neural Networks, IJCNN 2019 - Budapest, ハンガリー
継続期間: 7月 14 20197月 19 2019

出版物シリーズ

名前Proceedings of the International Joint Conference on Neural Networks
2019-July

会議

会議2019 International Joint Conference on Neural Networks, IJCNN 2019
国/地域ハンガリー
CityBudapest
Period7/14/197/19/19

!!!All Science Journal Classification (ASJC) codes

  • ソフトウェア
  • 人工知能

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