A Systematic Method for Classifying Software Requirements

Asmaa Hadi Al Bayati

Abstract


This paper focuses on the classification of software requirements to functional and non-functional categories using ensemble learning algorithms. It relies on the PROMISE dataset. The first stage involves preprocessing the dataset, which includes removing irrelevant columns, performing text processing, and applying natural language processing (NLP) methods like eliminating redundant phrases and two-letter words, and finally converting text to numbers using NLP techniques like eliminating redundant phrases and two-letter words and finally converting text to numbers using TF-IDF. The most important step for classification is extracting the properties based on the statistical technique of Analysis of Variance (ANOVA) so that the data becomes ready as inputs to machine learning algorithms. The paper employs four types of boosting algorithms. The results indicated that the XGBoosting algorithm is the best algorithm, with an accuracy of .95, compared to the paper that used the same database.

Keywords


classification; ensemble learning; functional requirements; intelligent techniques; non-functional requirements

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References


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DOI: https://doi.org/10.32520/stmsi.v15i10.6951

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