Fault-prone module detection using large-scale text features based on spam filtering

作者：Hata Hideaki; Mizuno Osamu^*; Kikuno Tohru

来源：Empirical Software Engineering, 2010, 15(2): 147-165.

DOI：10.1007/s10664-009-9117-9

• Fault-prone module
• Large-scale
• Text feature
• Spam filtering
• Text mining
• Software repository

This paper proposes an approach using large-scale text features for fault-prone module detection inspired by spam filtering. The number of every text feature in the source code of a module is counted and used as data for training detection models. In this paper, we prepared a naive Bayes classifier and a logistic regression model as detection models. To show the effectiveness of our approaches, we conducted experiments with five open source projects and compared them with a well-known metrics set, thereby achieving higher detection results. The results imply that large-scale text features are useful in constructing practical detection models, and measuring sophisticated metrics is not always necessary for detecting fault-prone modules.

• 出版日期2010-4