A Bayesian Network Based Approach for Change Coupling Prediction
A Bayesian Network Based Approach for Change Coupling Prediction
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DOI：
10.1109/wcre.2008.39
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发表时间：
2008-10
期刊：
影响因子：
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通讯作者：
Yu Zhou;Michael Würsch;E. Giger;H. Gall;Jianghu Lu
中科院分区：
文献类型：
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作者：
Yu Zhou;Michael Würsch;E. Giger;H. Gall;Jianghu Lu
源代码耦合和变更历史记录是两个重要的数据源用于变更耦合分析。近年来，公共开源项目的受欢迎程度使这两个来源都可用。根据我们以前的研究，在本文中，我们检查软件更改的不同维度，包括更改重要性或源代码依赖性级别，从两个来源中提取一组功能，并提出了一种基于贝叶斯网络的方法，以更改耦合预测。通过结合共同实体的特征及其依赖关系，该方法可以对潜在的不确定性进行建模。对两个中型开源项目的经验案例研究表明，与以前的工作相比，我们方法的可行性和有效性。
Source code coupling and change history are two important data sources for change coupling analysis. The popularity of public open source projects in recent years makes both sources available. Based on our previous research, in this paper, we inspect different dimensions of software changes including change significance or source code dependency levels, extract a set of features from the two sources and propose a Bayesian network-based approach for change coupling prediction. By combining the features from the co-changed entities and their dependency relation, the approach can model the underlying uncertainty. The empirical case study on two medium-sized open source projects demonstrates the feasibility and effectiveness of our approach compared to previous work.