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Empirical Software Engineering

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01-12-2022

FENSE: A feature-based ensemble modeling approach to cross-project just-in-time defect prediction

Authors: Tanghaoran Zhang, Yue Yu, Xinjun Mao, Yao Lu, Zhixing Li, Huaimin Wang

Published in: Empirical Software Engineering | Issue 7/2022

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Abstract

Context:

Just-in-time defect prediction (JITDP) leverages modern machine learning models to predict the defect-proneness of commits. Such models require adequate training data, which is unavailable in projects with short histories. To address this problem, cross-project methods reuse the data or models in other projects to make predictions, grounded on the assumption that they share similar defect-related features. However, these features are overlooked, which leads to unsatisfying model performance.

Objective:

This study aims to investigate the relationship between cross-project JITDP performances and project features, thereby improving the performance of cross-project models.

Method:

We propose a F eature-based ENSE mble modeling approach (FENSE) to cross-project JITDP. For a target project, FENSE pairs it to each source project and obtains 20 features. Leveraging them, it can predict the transferability of each off-the-shelf JITDP model. Then FENSE identifies the most transferable ones and combines them to make cross-project predictions. To achieve this, we conduct a large-scale empirical study of 113,906 project pairs in GitHub and investigate the impact of project features.

Results:

The results show that: (1) cross-project transferability is highly related to features including programming language and the defect ratio of the source project; (2) our feature-based model selection scheme can improve the cross-project JITDP performance by 10%; (3) FENSE outperforms other models on five evaluation measures without extra time and space costs.

Conclusions:

Our study suggests that project features can help identify powerful cross-project JITDP models and improve the performance of ensemble approaches.

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Title: FENSE: A feature-based ensemble modeling approach to cross-project just-in-time defect prediction
Authors: Tanghaoran Zhang
Yue Yu
Xinjun Mao
Yao Lu
Zhixing Li
Huaimin Wang
Publication date: 01-12-2022
Publisher: Springer US
Published in: Empirical Software Engineering / Issue 7/2022
Print ISSN: 1382-3256
Electronic ISSN: 1573-7616
DOI: https://doi.org/10.1007/s10664-022-10185-8

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Abstract

Context:

Objective:

Method:

Results:

Conclusions:

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