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DISTANCE METRIC BASED DIVERGENT CHANGE BAD SMELL DETECTION AND REFACTORING SCHEME ANALYSIS

机译:基于距离度量的发散变化差智能检测和重构方案分析

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摘要

Bad smells are signs of potential problems in codes. Bad smells decrease the design quality of software, so the codes are hard to analyze, understand, test or reuse. Divergent Change is a common and classical bad smell in object oriented programs. The detection of this bad smell is difficult, because the features of Divergent Change are not obvious, and the detecting and refactoring of this bad smell are on the later steps of software life cycle. In this paper, the detection method of Divergent Change bad smell based on distance metric and K-nearest neighbor clustering technology is proposed. The features of Divergent Change are analyzed and transformed to distances between entities. Divergent Change bad smells are detected with the results of K-nearest neighbor clustering, and targeted refactoring schemes are provided. After comparisons with similar researches, the experiments results on open source programs show that the proposed method behaves well on refactoring evaluation with low time complexity.
机译:难闻的气味是代码中潜在问题的迹象。难闻的气味降低了软件的设计质量,因此很难对代码进行分析,理解,测试或重用。发散变化是面向对象程序中常见的经典恶臭。很难检测到这种不良气味,因为发散变化的功能并不明显,并且在软件生命周期的后续步骤中检测和重构这种不良气味。提出了一种基于距离度量和K近邻聚类技术的发散性难闻气味检测方法。分析发散变化的特征并将其转换为实体之间的距离。通过K最近邻居聚类的结果检测到发散变化的难闻气味,并提供了针对性的重构方案。经过与类似研究的比较,在开源程序上的实验结果表明,该方法在重构评估中表现良好,时间复杂度低。

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