Effort estimation is a fairly researched field in the area of software engineering. Algorithmic and non-algorithmic methods are the two popular ways of estimating software development efforts. Various machine learning techniques are also being used to determine project efforts based on the historical project-related dataset. These techniques consume an array of project characteristics to estimate the project cost. The selection of the right technique to correctly determine the project cost is a significant challenge that the software industry is facing. This paper presents a fuzzy cognitive mapping (FCM) approach to recommend the best machine learning-based software estimation technique for Web applications. FCM shows synergistic interactions between system variables, and this property is used in the context of Web application estimation for suggesting an estimation technique based on the Web project configuration. To counter the ambiguity in defining abstract relationships between system variables, this article also proposes to incorporate fuzzy numbers. The current analysis involves using five different estimation techniques on 125 student project records. The mean square error (MSE) was taken as a performance metric to declare the supremacy of one estimation technique over others. The experimental results show that the selection of an effort estimation technique should not ignore the presence of project characteristics in the input vector. The achievement of this work is that the proposed technique is capable of recommending the suitable most Web estimation model based on project credentials for a specific Web project; it refrains from suggesting an estimation model optimum for the most project configurations. The FCM approach on software estimation technique recommendation results in a probability of success equals to 70%.

中文翻译：

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基于模糊认知映射分析的推荐基于Web学习的工作量估算技术

努力估算是软件工程领域中一个相当研究的领域。算法和非算法方法是估算软件开发工作量的两种流行方法。基于历史项目相关的数据集，各种机器学习技术也被用于确定项目工作。这些技术消耗了一系列项目特征来估计项目成本。选择正确的技术来正确确定项目成本是软件行业面临的重大挑战。本文提出了一种模糊认知映射（FCM）方法，为Web应用程序推荐基于最佳机器学习的软件估计技术。FCM显示系统变量之间的协同相互作用，此属性用于Web应用程序估计的上下文中，以建议基于Web项目配置的估计技术。为了解决在定义系统变量之间的抽象关系时的歧义，本文还建议引入模糊数。当前的分析涉及对125个学生项目记录使用五种不同的估算技术。均方误差（MSE）被用作一种性能指标，以宣告一种估算技术优于其他估算技术。实验结果表明，工作量估算技术的选择不应忽略输入矢量中项目特征的存在。这项工作的成果是，所提出的技术能够基于特定Web项目的项目凭据来推荐最适合的Web评价模型。它避免了为大多数项目配置建议最佳的估算模型。基于FCM的软件估计技术推荐方法得出的成功概率等于70％。