In Requirement Engineering, software requirements are classified into two main categories: Functional Requirement (FR) and Non-Functional Requirement (NFR). FR describes user and system goals. NFR includes all constraints on services and functions. Deeper classification of those two categories facilitates the software development process. There are many techniques for classifying FR; some of them are Machine Learning (ML) techniques, and others are traditional. To date, the classification accuracy has not been satisfactory. In this paper, we introduce a new ensemble ML technique for classifying FR statements to improve their accuracy and availability. This technique combines different ML models and uses enhanced accuracy as a weight in the weighted ensemble voting approach. The five combined models are Naive Bayes, Support Vector Machine (SVM), Decision Tree, Logistic Regression, and Support Vector Classification (SVC). The technique was implemented, trained, and tested using a collected dataset. The accuracy of classifying FR was 99.45%, and the required time was 0.7 s.

中文翻译：

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一种用于功能需求分类的集成机器学习技术

在需求工程中，软件需求分为两大类：功能需求（FR）和非功能需求（NFR）。FR 描述了用户和系统目标。NFR 包括对服务和功能的所有限制。对这两个类别进行更深入的分类有利于软件开发过程。FR 的分类技术有很多；其中一些是机器学习 (ML) 技术，而另一些则是传统技术。迄今为止，分类准确度一直不令人满意。在本文中，我们介绍了一种新的集成 ML 技术，用于对 FR 语句进行分类，以提高其准确性和可用性。该技术结合了不同的 ML 模型，并在加权集成投票方法中使用增强的准确性作为权重。五个组合模型是朴素贝叶斯、支持向量机 (SVM)、决策树、逻辑回归和支持向量分类 (SVC)。使用收集的数据集实施、训练和测试该技术。FR分类准确率为99.45%，所需时间为0.7 s。