Control Flow Graph Embedding Based on Multi-Instance Decomposition for Bug Localization

Abstract

During software maintenance, bug report is an effective way to identify potential bugs hidden in a software system. It is a great challenge to automatically locate the potential buggy source code according to a bug report. Traditional approaches usually represent bug reports and source code from a lexical perspective to measure their similarities. Recently, some deep learning models are proposed to learn the unified features by exploiting the local and sequential nature, which overcomes the difficulty in modeling the difference between natural and programming languages. However, only considering local and sequential information from one dimension is not enough to represent the semantics, some multi-dimension information such as structural and functional nature that carries additional semantics has not been well-captured. Such information beyond the lexical and structural terms is extremely vital in modeling program functionalities and behaviors, leading to a better representation for identifying buggy source code. In this paper, we propose a novel model named CG-CNN, which is a multi-instance learning framework that enhances the unified features for bug localization by exploiting structural and sequential nature from the control flow graph. Experimental results on widely-used software projects demonstrate the effectiveness of our proposed CG-CNN model.