# Defect Prediction with Content-based Features

arXiv: https://arxiv.org/abs/2409.18365 · HTML: https://mlsanity.com/paper/2409.18365/

## Key facts

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- First posted on arXiv 27 Sep 2024.

## Abstract (arXiv)

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.