用代码内容特征预测缺陷，效果优于传统复杂度指标
Defect Prediction with Content-based Features
- 从代码内容提取词汇、类型等特征进行缺陷预测
- 内容特征预测准确率高于代码复杂度指标
- 特征筛选与组合能进一步提升预测效果
传统缺陷预测方法多依赖代码复杂度等度量指标，如源文件行数。本文提出一种基于代码内容的新方法：假设软件代码蕴含其技术特性信息，而这些特性可能具有不同缺陷倾向性。因此，从源码文件中提取的词汇、主题、数据类型、包名等内容特征可用于缺陷预测。我们进行了广泛的实证评估，结果表明：i) 内容特征的预测能力高于代码复杂度指标；ii) 使用特征选择、降维和组合可进一步提升预测性能。
原文摘要 · Abstract (English)
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.
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