Bug prediction based on fine-grained module histories

Hideaki Hata, O. Mizuno, T. Kikuno
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引用次数: 124

Abstract

There have been many bug prediction models built with historical metrics, which are mined from version histories of software modules. Many studies have reported the effectiveness of these historical metrics. For prediction levels, most studies have targeted package and file levels. Prediction on a fine-grained level, which represents the method level, is required because there may be interesting results compared to coarse-grained (package and file levels) prediction. These results include good performance when considering quality assurance efforts, and new findings about the correlations between bugs and histories. However, fine-grained prediction has been a challenge because obtaining method histories from existing version control systems is a difficult problem. To tackle this problem, we have developed a fine-grained version control system for Java, Historage. With this system, we target Java software and conduct fine-grained prediction with well-known historical metrics. The results indicate that fine-grained (method-level) prediction outperforms coarse-grained (package and file levels) prediction when taking the efforts necessary to find bugs into account. Using a correlation analysis, we show that past bug information does not contribute to method-level bug prediction.
基于细粒度模块历史的Bug预测
有很多bug预测模型是用历史指标构建的,这些指标是从软件模块的版本历史中挖掘出来的。许多研究报告了这些历史指标的有效性。对于预测级别,大多数研究都针对包和文件级别。需要在细粒度级别(代表方法级别)上进行预测,因为与粗粒度(包和文件级别)预测相比,可能会有有趣的结果。当考虑质量保证工作时,这些结果包括良好的性能,以及关于错误和历史之间相关性的新发现。然而,细粒度预测一直是一个挑战,因为从现有的版本控制系统获取方法历史是一个难题。为了解决这个问题,我们为Java开发了一个细粒度的版本控制系统,Historage。有了这个系统,我们以Java软件为目标,并使用众所周知的历史指标进行细粒度预测。结果表明,细粒度(方法级)预测比粗粒度(包和文件级)预测更有效。通过相关分析,我们发现过去的bug信息对方法级bug预测没有贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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