Defect prediction is one of the most popular research topics due to its potential to minimize software quality assurance efforts. Existing approaches have examined defect prediction from various perspectives such as complexity and developer metrics. However, none of these consider programming style for defect prediction. This paper aims at analyzing the impact of stylistic metrics on both within-project and crossproject defect prediction. For prediction, 4 widely used machine learning algorithms namely Naive Bayes, Support Vector Machine, Decision Tree and Logistic Regression are used. The experiment is conducted on 14 releases of 5 popular, open source projects. F1, Precision and Recall are inspected to evaluate the results. Results reveal that stylistic metrics are a good predictor of defects.

翻译：现有方法从复杂度和开发量等不同角度审查了缺陷预测,但其中没有一个考虑缺陷预测的编程风格,本文旨在分析标准度量指标对项目内和跨项目缺陷预测的影响,在预测方面,使用了四种广泛使用的机器学习算法,即纳米贝耶斯、支持矢量机、决定树和物流倒退,对五个开放源项目的14种发布进行了实验。 F1、精密度和回召都进行了检查,以评价结果。结果显示,标准度量值是缺陷的良好预测。