软件缺陷自动分类方法

2012 Sixth International Conference on Complex, Intelligent, and Software Intensive Systems Pub Date : 2012-07-04 DOI:10.1109/CISIS.2012.132

Neelofar, M. Javed, Hufsa Mohsin

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引用次数: 20

摘要

像Eclipse和firefox这样的开源项目都有开源的bug存储库。用户向这些存储库报告bug。这些存储库的用户通常是非技术人员，无法为这些错误分配正确的类。对开发人员来说，对bug进行分类和修复是一项冗长而耗时的任务。开发人员通常是特定领域的专家。例如，很少有开发人员精通GUI，而其他人则精通java功能。将特定的错误分配给相关的开发人员可以节省时间，并且通过根据开发人员的兴趣分配错误，有助于保持开发人员的兴趣水平。然而，在不知道错误所属的实际类的情况下，将正确的错误分配给正确的开发人员是相当困难的。在本研究中，我们在对bug进行总结的基础上，对bug进行了不同标签的分类。多项Naïve贝叶斯文本分类器用于分类目的。特征选择采用卡方算法和TFIDF算法。使用Naïve贝叶斯和卡方，我们得到83%的平均准确率。

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An Automated Approach for Software Bug Classification

Open source projects for example Eclipse and Fire fox have open source bug repositories. User reports bugs to these repositories. Users of these repositories are usually non-technical and cannot assign correct class to these bugs. Triaging of bugs, to developer, to fix them is a tedious and time consuming task. Developers are usually expert in particular areas. For example, few developers are expert in GUI and others are in java functionality. Assigning a particular bug to relevant developer could save time and would help to maintain the interest level of developers by assigning bugs according to their interest. However, assigning right bug to right developer is quite difficult for triager without knowing the actual class, the bug belongs to. In this research, we have classified the bugs in different labels on the basis of summary of the bug. Multinomial Naïve Bayes text classifier is used for classification purpose. For feature selection, Chi-Square and TFIDF algorithms were used. Using Naïve Bayes and Chi-square, we get average of 83 % accuracy.

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来源期刊

2012 Sixth International Conference on Complex, Intelligent, and Software Intensive Systems

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