Cross-project Defect Prediction via ASTToken2Vec and BLSTM-based Neural Network
| Autor: | Xiaohong Li, Xiang Chen, Hao Li, Xiaofei Xie, Yanzhou Mu, Zhiyong Feng |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Artificial neural network
Computer science business.industry Deep learning Node (networking) 020207 software engineering 02 engineering and technology Construct (python library) Machine learning computer.software_genre Variable (computer science) Software 020204 information systems 0202 electrical engineering electronic engineering information engineering Embedding Artificial intelligence Abstract syntax tree business computer |
| Zdroj: | IJCNN |
| DOI: | 10.1109/ijcnn.2019.8852135 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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