Semi-Automated Classification of Non-Functional Arabic User Requirements using NLP Tools

Abstract

Requirements Engineering is a critical phase in the software development life cycle, encompassing both Functional Requirements (FR) and Non-Functional Requirements (NFR). NFR defines the quality attributes of the system, including performance, security, availability, look and feel, fault tolerance, legal and operational, essential for meeting user needs and imposing additional constraints on software quality. Prioritizing NFR from user requirements is challenging, requiring specialized skills and domain knowledge. Manual categorization is time-consuming and mentally taxing for developers, making automated or semi-automated classification of NFR from requirements documents valuable. This approach reduces manual effort and time in identifying specific NFR among numerous requirements. This thesis introduces a novel semi-automated categorization approach for Arabic non-functional user requirements using CAMeL Tools, a natural language processing tool. We propose a set of heuristics based on fundamental Arabic sentence constructions to extract information and categorize requirements into seven NFR classes. Tokens, PoS tags, and lemmas of parsed user requirements are generated using CAMeL tools. The closest class for each statement is determined by applying heuristic criteria to CAMeL outputs. The implementation of our approach using CAMeL Tools 1.3.1 and Python code in a Windows 10 environment demonstrates its practical applicability and efficiency in classifying Arabic non-functional user requirements