Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

5-2022

Abstract

Just-In-Time (JIT) defect prediction aims to automatically predict whether a commit is defective or not, and has been widely studied in recent years. In general, most studies can be classified into two categories: 1) simple models using traditional machine learning classifiers with hand-crafted features, and 2) complex models using deep learning techniques to automatically extract features. Hand-crafted features used by simple models are based on expert knowledge but may not fully represent the semantic meaning of the commits. On the other hand, deep learning-based features used by complex models represent the semantic meaning of commits but may not reflect useful expert knowledge. Simple models and complex models seem complementary to each other to some extent. To utilize the advantages of both simple and complex models, we propose a combined model namely SimCom by fusing the prediction scores of one simple and one complex model. The experimental results show that our approach can significantly outperform the state-of-the-art by 6.0-18.1%. In addition, our experimental results confirm that the simple model and complex model are complementary to each other.

Keywords

Deep learning, Semantics, Predictive models, Feature extraction

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the 30th International Conference on Program Comprehension, Virtual Event, 2022 May 16-17

First Page

229

Last Page

240

Identifier

10.1145/3524610.3527910

Publisher

Institute of Electrical and Electronics Engineers

City or Country

New Jersey

Additional URL

https://doi.org/10.1145/3524610.3527910

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