Improving bug detection and fixing via code representation learning

Document Type

Conference Proceeding

Publication Date

6-27-2020

Abstract

The software quality and reliability have been proved to be important during the program development. There are many existingstudies trying to help improve it on bug detection and automatedprogram repair processes. However, each of them has its own limitation and the overall performance still have some improvementspace. In this paper, we proposed a deep learning framework toimprove the software quality and reliability on these two detectfix processes. We used advanced code modeling and AI models tohave some improvements on the state-of-the-art approaches. Theevaluation results show that our approach can have a relative improvement up to 206% in terms of F-1 score when comparing withbaselines on bug detection and can have a relative improvementup to 19.8 times on the correct bug-fixing amount when comparing with baselines on automated program repair. These results canprove that our framework can have an outstanding performanceon improving software quality and reliability in bug detection andautomated program repair processes.

Identifier

85094145554 (Scopus)

ISBN

[9781450371223]

Publication Title

Proceedings International Conference on Software Engineering

External Full Text Location

https://doi.org/10.1145/3377812.3382172

ISSN

02705257

First Page

137

Last Page

139

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