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Improving the Precision of Fowler's Definitions of Bad Smells

机译:提高福勒对令人难闻的味道定义的精确度

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Current approaches to detecting Bad Smells in code are mainly based on software metrics. We suggest that these methods lack precision in detecting Bad Smells, and we propose a code pattern-based approach to detecting Bad Smells. However before such a pattern-based approach can be implemented, Fowler's original definitions of Bad Smells need to be made more precise. Currently Fowler's definitions are too informal to implement in a pattern-searching tool. In this paper we use an expert panel to evaluate our enhanced definitions for five of Fowler's Bad Smells. We use a questionnaire to survey four experts' opinions of our Bad Smell definitions. Our results show that the experts basically agree with our enhanced definitions of the Message Chains, Middle Man and Speculative Generality Bad Smells. However, there are strong disagreements on our definitions of the Data Clumps and Switch Statements Bad Smells. We present enhanced definitions on the basis of these expert opinions.
机译:在代码中检测到难闻气味的目前的方法主要基于软件度量。我们建议这些方法缺乏检测臭味的精确度,并提出了一种基于代码模式的方法来检测难闻的气味。然而,在这种基于模式的方法之前可以实施之前,需要更精确地制作Fowler的原始定义。目前,Fowler的定义太非正式地在模式搜索工具中实现。在本文中,我们使用专家小组来评估我们的增强的定义,以为五个福勒的糟糕闻起来。我们使用调查问卷调查四位专家对我们糟糕的味道定义的看法。我们的研究结果表明,专家基本上同意我们的信息链,中间人和投机性普遍性味道的增强定义。但是,对我们对数据集成的定义和交换机陈述的定义有很大的分歧。我们在这些专家意见的基础上提出了增强的定义。

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