一种推导软件产品线度量阈值的方法

Gustavo Vale, Eduardo Figueiredo
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引用次数: 29

摘要

软件产品线(SPL)是一组软件系统,它们共享一组通用且可变的组件(特性)。软件度量提供了量化SPL组件的几个质量方面的基本方法。然而,声压级测量过程的有效性直接取决于可靠阈值的定义。如果没有正确定义阈值,则很难实际知道给定的度量值是否表明组件实现中的潜在问题。有几种方法可以推导软件度量的阈值。然而,人们对它们是否适合SPLs的背景了解甚少。本文旨在提出一种在SPL环境下推导阈值的方法。我们的方法使用两种代码气味(God Class和Lazy Class)检测策略来评估召回率和精确度。对我们的方法进行评估是基于33个SPLs的基准,并将结果与在SPLs背景下使用的具有相同目的的方法(基线)进行比较(未提议)。结果表明,与基线相比,我们的方法具有更好的召回率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Method to Derive Metric Thresholds for Software Product Lines
A software product line (SPL) is a set of software systems that share a common and variable set of components (features). Software metrics provide basic means to quantify several quality aspects of SPL components. However, the effectiveness of the SPL measurement process is directly dependent on the definition of reliable thresholds. If thresholds are not properly defined, it is difficult to actually know whether a given metric value indicates a potential problem in the component implementation. There are several methods to derive thresholds for software metrics. However, there is little understanding about their appropriateness for the context of SPLs. This paper aims to propose a method to derive thresholds in the SPL context. Our method is evaluated in terms of recall and precision using two code smells (God Class and Lazy Class) detection strategies. The evaluation of our method is performed based on a benchmark of 33 SPLs and the results were compared with a method (baseline) with the same purpose used in the context of SPLs (not proposed). The results show that our method has better recall when compared with baseline.
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