Automated Classification of Software Project Requirements: A Machine Learning-Based Approach

Abstract

Accurate categorization of software requirements into security (SR) and non-security (NSR) categories is crucial for project management and decision-making. The traditional categorization is time-taken and susceptible to mistakes nature, necessitating an automated solution. This study investigates the automatic labeling of requirement sentences by utilizing TF-IDF in conjunction with Individual Keyword Comparison (IKC) and Combined Keyword Comparison (CKC) methods, with validation performed using machine learning models, including Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF). This research identifies effective techniques and models for SR classification, enhancing efficiency and accuracy in software engineering processes. This research advances data prepossessing and SR classification methodologies, providing insights for improved decision-making in software development projects.Additionally, my method ASBL (Automatic Score-Based Labeling) achieves a training accuracy of 92% when validated through machine learning SVM after automatically labeling requirements of the combined dataset of DOSSPRE and PROMISE into security and non-security categories. Furthermore, an accuracy of up to 81% was demonstrated when the model was tested by classifying the project requirements of MCS final year students.