Defect Prediction with Content-based Features
This work addresses defect prediction for software developers, offering an incremental improvement over existing methods.
The paper tackled software defect prediction by using content-based features from source code, such as words and topics, and found they have higher predictive power than traditional complexity metrics, with further improvements from feature selection and combination.
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.