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An empirical study of the sensitivity of quality indicator for software module clustering

机译：质量指标对软件模块聚类敏感性的实证研究

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Recently, there has been a significant progress in applying evolutionary multiobjective optimization techniques to solve software module clustering problem. The results of evolutionary multiobjective optimization techniques for software module clustering problem are a set of many non-dominating clustering solutions. Generally, the quality indicators of clustering solutions produced by these techniques are sensitive to minor variation in the decision variables of the clustering solutions. Researchers have focused on finding software module clustering with better cluster quality indicator; however in practice developers may not always be interested to better quality indicator clustering solutions, particularly if these quality indicators are quite sensitive. Under such situations, developer looks for clustering solutions whose quality indicators are not sensitive to small variations in the decision variables of the candidate clustering solution. The paper performs an experiment for the sensitivity analysis of quality indicator on software module clustering solution with two multiobjective formulations MCA and ECA. To perform the experiment the NSGA-II is used as multi-objective evolutionary algorithm. We evaluate sensitivity of quality indicators for six real-world software and one random problem. Results indicate that the quality indicator for MCA formulation is less sensitive than ECA formulation and hence MCA will be a better choice for multiobjective software module clustering from sensitivity perspective.
机译：最近，在应用进化多目标优化技术解决软件模块聚类问题方面取得了重大进展。用于软件模块聚类问题的进化多目标优化技术的结果是一组许多非支配性聚类解决方案。通常，通过这些技术产生的聚类解决方案的质量指标对聚类解决方案的决策变量的细微变化敏感。研究人员致力于发现具有更好集群质量指标的软件模块集群。但是，实际上，开发人员可能并不总是对更好的质量指标聚类解决方案感兴趣，特别是在这些质量指标非常敏感的情况下。在这种情况下，开发人员将寻找质量指标对候选聚类解决方案的决策变量的微小变化不敏感的聚类解决方案。本文针对包含两个多目标公式MCA和ECA的软件模块聚类解决方案，对质量指标的敏感性进行了实验。为了进行实验，将NSGA-II用作多目标进化算法。我们评估了六个实际软件和一个随机问题的质量指标的敏感性。结果表明，MCA配方的质量指标不如ECA配方敏感，因此从灵敏度的角度来看，MCA将是多目标软件模块聚类的更好选择。

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