A deep tree-based model for software defect prediction

TL;DR

This paper introduces a deep tree-based neural network model that automatically learns source code features for defect prediction, improving accuracy over traditional feature-based methods.

Contribution

It presents a novel deep learning model leveraging tree-structured LSTM to directly utilize source code syntax for defect prediction, capturing semantic information more effectively.

Findings

Effective in within-project defect prediction

Successful cross-project prediction results

Outperforms traditional feature-based approaches

Abstract

Defects are common in software systems and can potentially cause various problems to software users. Different methods have been developed to quickly predict the most likely locations of defects in large code bases. Most of them focus on designing features (e.g. complexity metrics) that correlate with potentially defective code. Those approaches however do not sufficiently capture the syntax and different levels of semantics of source code, an important capability for building accurate prediction models. In this paper, we develop a novel prediction model which is capable of automatically learning features for representing source code and using them for defect prediction. Our prediction system is built upon the powerful deep learning, tree-structured Long Short Term Memory network which directly matches with the Abstract Syntax Tree representation of source code. An evaluation on two datasets, one from open source projects contributed by Samsung and the other from the public PROMISE repository, demonstrates the effectiveness of our approach for both within-project and cross-project predictions.