Recommendation of Machine Learning Techniques for Software Effort Estimation using Multi-Criteria Decision Making

Ajay Kumar
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Abstract

 For the development of the software industry, Software Effort Estimation (SEE) is one of the essential tasks. Project managers can overcome budget and time overrun issues by accurately estimating a software project's development effort in the software life cycle. In prior studies, a variety of machine learning methods for SEE modeling were applied. The outcomes for various performance or accuracy measures are inconclusive. Therefore, a mechanism for assessing machine learning approaches for SEE modeling in the context of several contradictory accuracy measures is desperately needed. This study addresses selecting the most appropriate machine learning technique for SEE modeling as a Multi-Criteria Decision Making (MCDM) problem. The machine learning techniques are selected through a novel approach based on MCDM. In the proposed approach, three MCDM methods- Weighted Aggregated Sum Product Assessment (WASPAS), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) were applied to determine the ranking of machine learning techniques on SEE performance based on multiple conflicting accuracy measures. For validating the proposed method, an experimental study was conducted over three SEE datasets using ten machine-learning techniques and six performance measures. Based on MCDM rankings, Random Forest, Support Vector Regression, and Kstar are recommended as the most appropriate machine learning techniques for SEE modeling. The results show how effectively the suggested MCDM-based approach can be used to recommend the appropriate machine learning technique for SEE modeling while considering various competing accuracy or performance measures altogether.
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利用多标准决策为软件工作量估算推荐机器学习技术
软件产业的发展离不开软件工作量估算(SEE)。项目经理可以通过准确估算软件生命周期中软件项目的开发工作量来克服预算和时间超支问题。在之前的研究中,人们应用了多种机器学习方法进行 SEE 建模。各种性能或准确性衡量标准的结果都没有定论。因此,亟需一种机制,用于评估在几种相互矛盾的准确性衡量标准背景下的 SEE 建模机器学习方法。本研究将为 SEE 建模选择最合适的机器学习技术作为一个多标准决策(MCDM)问题。机器学习技术是通过一种基于 MCDM 的新方法进行选择的。在所提出的方法中,应用了三种 MCDM 方法--加权汇总产品评估(WASPAS)、与理想解决方案相似性排序偏好技术(TOPSIS)和 VIseKriterijumska Optimizacija I Kompromisno Resenje(VIKOR),以根据多个相互冲突的准确性指标确定机器学习技术在 SEE 性能方面的排名。为了验证所提出的方法,使用十种机器学习技术和六种性能指标对三个 SEE 数据集进行了实验研究。根据 MCDM 排序,随机森林、支持向量回归和 Kstar 被推荐为最适合 SEE 建模的机器学习技术。结果表明,建议的基于 MCDM 的方法可以有效地为 SEE 建模推荐合适的机器学习技术,同时还能综合考虑各种相互竞争的准确性或性能指标。
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