Automating the Classification of Requirements Data

This paper proposes a pilot approach based on the comparative analysis of supervised Machine Learning models coupled with basic Natural Language Processing concepts for classifying Functional and Non-Functional Requirements from huge collections of data relevant to the Requirements Engineering (RE) phase within software development. The publicly available PROMISE Software Engineering Repository dataset is used in the execution of this approach. Non-Functional Requirements are further classified into subclasses based on attributes they address since they are not directly related to the core functions of the concerned software. This overall research initiative helps to make the RE phase more efficient and reduces human effort in software development. It leverages Big Data in Software Engineering.