Automated classification of software change messages by semi-supervised Latent Dirichlet Allocation
Automated classification of software change messages by semi-supervised Latent Dirichlet Allocation
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DOI：
10.1016/j.infsof.2014.05.017
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Ying Fu;Meng Yan;Xiaohong Zhang;Ling Xu;Dan Yang;Jeffrey D. Kymer
中科院分区：
文献类型：
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作者：
Ying Fu;Meng Yan;Xiaohong Zhang;Ling Xu;Dan Yang;Jeffrey D. Kymer
上下文主题模型，如概率潜在语义分析（pLSA）和潜在狄利克雷分配（LDA）已被证明是成功的挖掘软件存储库的任务。理解软件变更消息所描述的非结构化的自然语言的文本是挖掘这些消息在repositions.Meet.We寻求提出一种新的自动变更消息分类方法，其特征在于半监督主题semantic analysis.MethodIn这项工作中，我们提出了一种半监督LDA为基础的方法来自动分类变更消息的根本挑战之一。我们利用软件变化的领域知识来标记样本，并将其添加到半监督LDA模型中。接下来，我们验证了我们的方法在三个开源项目上的跨项目分析应用。与现有的软件变更分类方法相比，我们的方法有两个优点：首先，它缓解了如何设置适当数量的潜在主题的问题。在我们的方法中，我们不必选择潜在主题的数量，因为它对应于类标签的数量。第二、结果在实验中，我们的方法自动分类了约85%的变化信息，有效性调查显示，70.56%的变化信息被自动分类。的时间，我们的自动分类结果是在与开发人员的意见一致。结论我们的方法自动分类的变化消息，记录了软件变更的原因，本发明适用于软件变更消息的跨项目分析。
ContextTopic models such as probabilistic Latent Semantic Analysis (pLSA) and Latent Dirichlet Allocation (LDA) have demonstrated success in mining software repository tasks. Understanding software change messages described by the unstructured nature-language text is one of the fundamental challenges in mining these messages in repositories.ObjectiveWe seek to present a novel automatic change message classification method characterized by semi-supervised topic semantic analysis.MethodIn this work, we present a semi-supervised LDA based approach to automatically classify change messages. We use domain knowledge of software changes to make labeled samples which are added to build the semi-supervised LDA model. Next, we verify the cross-project analysis application of our method on three open-source projects. Our method has two advantages over existing software change classification methods: First of all, it mitigates the issue of how to set the appropriate number of latent topics. We do not have to choose the number of latent topics in our method, because it corresponds to the number of class labels. Second, this approach utilizes the information provided by the label samples in the training set.ResultsOur method automatically classified about 85% of the change messages in our experiment and our validation survey showed that 70.56% of the time our automatic classification results were in agreement with developer opinions.ConclusionOur approach automatically classifies most of the change messages which record the cause of the software change and the method is applicable to cross-project analysis of software change messages.