An Ensemble Machine Learning Technique for Functional Requirement Classification

Nouf Rahimi,Fathy Eassa,Lamiaa Elrefaei

doi:10.3390/sym12101601

Nouf Rahimi, Fathy Eassa + Show 1 more

Open Access

https://doi.org/10.3390/sym12101601

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Abstract

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 Naïve 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.

Highlights

There are different definitions of requirements in different books and manuals
This paper introduces an enhanced technique for weighted ensemble voting in Machine Learning (ML) to classify Functional Requirement (FR) into multiple classes
We provide the training settings and the results of the testing phase according to the percentage of the dataset split between training and testing (70–30%)

Summary

Introduction

There are different definitions of requirements in different books and manuals. Considering the definition of the Institute of Electrical and Electronics Engineers (IEEE) standards, a requirement is a capability or condition needed by a user or a system to satisfy an objective [1].Software requirement classification affects the other activities of Software Development (SD).For example, prioritization—the filtering of relevant requirements—is facilitated by effective classification [2]. There are different definitions of requirements in different books and manuals. Considering the definition of the Institute of Electrical and Electronics Engineers (IEEE) standards, a requirement is a capability or condition needed by a user or a system to satisfy an objective [1]. NFR includes all constraints on services and functions [3]. Deeper classification of these two categories can facilitate the SD process [3]. The most common categories of NFR in the reviewed papers can be categorized into four to eleven classes. These classes include maintainability, operability, performance, security, usability, and reliability

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