EPR: a Neural Network for Automatic Feature Learning from Code for Defect Prediction
Dingbang Fang, Shaoying Liu, Liu Ai · 2021 IEEE 21st International Conference on Software Quality, Reliability and Security (QRS) · 2021
Software defect prediction plays a significant role in the software development cycle but suffers from many difficulties. In this paper we propose a novel deep learning model (including algorithms) called Extractor, Parser, and Reviewer (EPR) for defect prediction in software. Two different networks, recurrent neural networks (RNNs) and one-dimensional convolutional networks(ODCNs), are employed by the EPR for different purposes. RNN is utilized to extract contextual features to represent semantic dependencies between code tokens and ODCN acts as a parser to establish dependencies between semantic features. Meanwhile, the attention mechanism of the two networks is used as a reviewer to assign different weights from location information to the importance of the features, respectively. Our proposed model is validated by the PROMISE repository, and the results show that the proposed model in this paper significantly outperforms several existing algorithms.