Defect Prediction with Content-based Features
<|reference_start|>Defect Prediction with Content-based Features: 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.<|reference_end|>
@article{pham2024defect,
    title={Defect Prediction with Content-based Features},
    author={Hung Viet Pham and Tung Thanh Nguyen},
    journal={arXiv preprint arXiv:2409.18365},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.18365},
    primaryClass={cs.SE cs.CL cs.LG}
}