Defect Prediction with Content-based Features
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917788218556416 |
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| author | Pham, Hung Viet Nguyen, Tung Thanh |
| author_facet | Pham, Hung Viet Nguyen, Tung Thanh |
| contents | Traditional defect prediction approaches often use metrics that measure the complexity of the design or implementing code of a software system, such as the number of lines of code in a source file. In this paper, we explore a different approach based on content of source code. Our key assumption is that source code of a software system contains information about its technical aspects and those aspects might have different levels of defect-proneness. Thus, content-based features such as words, topics, data types, and package names extracted from a source code file could be used to predict its defects. We have performed an extensive empirical evaluation and found that: i) such content-based features have higher predictive power than code complexity metrics and ii) the use of feature selection, reduction, and combination further improves the prediction performance. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2409_18365 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Defect Prediction with Content-based Features Pham, Hung Viet Nguyen, Tung Thanh Software Engineering Computation and Language Machine Learning Traditional defect prediction approaches often use metrics that measure the complexity of the design or implementing code of a software system, such as the number of lines of code in a source file. In this paper, we explore a different approach based on content of source code. Our key assumption is that source code of a software system contains information about its technical aspects and those aspects might have different levels of defect-proneness. Thus, content-based features such as words, topics, data types, and package names extracted from a source code file could be used to predict its defects. We have performed an extensive empirical evaluation and found that: i) such content-based features have higher predictive power than code complexity metrics and ii) the use of feature selection, reduction, and combination further improves the prediction performance. |
| title | Defect Prediction with Content-based Features |
| topic | Software Engineering Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2409.18365 |