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Identifying key classes of object-oriented software based on software complex network

机译:基于软件复杂网络的面向对象软件关键类别识别

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Identifying the most important classes in a software system is crucial for engineers to understand or maintain an unfamiliar system. Complex network theory provides a new way to study the characteristics of large-scale software. Unfortunately, most current studies consider only one or a limited number of factors affecting software structure, rendering inaccurate the results of mining the key classes of software. Thus, we propose an approach using various complex network metrics to automatically identify key classes from global and local aspects. From the global aspect, the location of a class and its ability to control the information flow of software are mainly considered. From local aspects, we focus on the interactions of classes with their neighbors, as well as the complexity of the class itself. Experiments are performed on two java open-source projects. Results show that this approach can accurately identify key classes compared with existing literature.
机译:识别软件系统中最重要的类对于工程师理解或维护不熟悉的系统至关重要。复杂网络理论为研究大型软件的特性提供了新的途径。不幸的是,当前大多数研究仅考虑影响软件结构的一个或有限数量的因素,从而导致对关键软件类别的挖掘结果不准确。因此,我们提出了一种使用各种复杂的网络指标从全球和本地方面自动识别关键类别的方法。从全局的角度来看,主要考虑类的位置及其控制软件信息流的能力。从本地角度来看,我们着重于类与其邻居的交互以及类本身的复杂性。实验是在两个Java开源项目上进行的。结果表明,与现有文献相比,该方法可以准确地识别关键类别。

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