Boosting just-in-time defect prediction with specific features of C/C++ programming languages in code changes

Publication Type

Conference Proceeding Article

Publication Date

5-2023

Abstract

Just-in-time (JIT) defect prediction can identify changes as defect-inducing ones or clean ones and many approaches are proposed based on several programming language-independent change-level features. However, different programming languages have different characteristics and consequently may affect the quality of software projects. Meanwhile, the C programming language, one of the most popular ones, is widely used to develop foundation applications (i.e., operating system, database, compiler, etc.) in IT companies and its change-level characteristics on project quality have not been fully investigated. Additionally, whether open-source C projects have similar important features to commercial projects has not been studied much.To address the aforementioned limitations, in this paper, we investigate the impacts of programming language-specific features on the state-of-the-art JIT defect identification approach in an industrial setting. We collect and label the top-10 most starred C projects (i.e., 329,021 commits) on GitHub and 8 C projects in an ICT company (i.e., 12,983 commits). We also propose nine C-specific change-level features and focus our investigations on both open-source C projects on GitHub and C projects at the ICT company considering three aspects: (1) The effectiveness of C-specific change-level features in improving the performance of identification of defect-inducing changes, (2) The importance of features in the identification of defect-inducing changes between open-source C projects and commercial C projects, and (3) The effectiveness of combining language-independent features and C-specific features in a real-life setting at the ICT company.

Keywords

C++ programming, C/C++ programming language, Code changes, Defect prediction, Just-in-time, Language independents, Open-source, Quality of software, Software project, Supervised methods

Discipline

Databases and Information Systems | Programming Languages and Compilers | Software Engineering

Publication

Proceedings of the 20th IEEE/ACM International Conference on Mining Software Repositories, Melbourne, Australia, 2023 May 15-16

First Page

472

Last Page

484

ISBN

9798350311846

Identifier

10.1109/MSR59073.2023.00072

Publisher

IEEE

City or Country

New Jersey

Additional URL

https://doi.org/10.1109/MSR59073.2023.00072

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