Open Access

ARTICLE

Towards Improving the Quality of Requirement and Testing Process in Agile Software Development: An Empirical Study

by Irum Ilays¹, Yaser Hafeez^1,*, Nabil Almashfi², Sadia Ali¹, Mamoona Humayun^3,*, Muhammad Aqib¹, Ghadah Alwakid⁴

1 University Institute of Information Technology, Pir Mehr Ali Shah Arid Agriculture University, Rawalpindi, 46000, Pakistan
2 Department of Software Engineering, College of Computer and Information Sciences, Jouf University, Al Jouf, 72388, Saudi Arabia
3 School of Arts Humanities and Social Sciences, University of Roehampton, London, SW15 5PJ, UK
4 Department of Computer Science, College of Computer and Information Sciences, Jouf University, Al Jouf, 72388, Saudi Arabia

* Corresponding Authors: Yaser Hafeez. Email: ; Mamoona Humayun. Email:

(This article belongs to the Special Issue: Requirements Engineering: Bridging Theory, Research and Practice)

Computers, Materials & Continua 2024, 80(3), 3761-3784. https://doi.org/10.32604/cmc.2024.053830

Received 10 May 2024; Accepted 26 July 2024; Issue published 12 September 2024

Abstract

Software testing is a critical phase due to misconceptions about ambiguities in the requirements during specification, which affect the testing process. Therefore, it is difficult to identify all faults in software. As requirement changes continuously, it increases the irrelevancy and redundancy during testing. Due to these challenges; fault detection capability decreases and there arises a need to improve the testing process, which is based on changes in requirements specification. In this research, we have developed a model to resolve testing challenges through requirement prioritization and prediction in an agile-based environment. The research objective is to identify the most relevant and meaningful requirements through semantic analysis for correct change analysis. Then compute the similarity of requirements through case-based reasoning, which predicted the requirements for reuse and restricted to error-based requirements. Afterward, the apriori algorithm mapped out requirement frequency to select relevant test cases based on frequently reused or not reused test cases to increase the fault detection rate. Furthermore, the proposed model was evaluated by conducting experiments. The results showed that requirement redundancy and irrelevancy improved due to semantic analysis, which correctly predicted the requirements, increasing the fault detection rate and resulting in high user satisfaction. The predicted requirements are mapped into test cases, increasing the fault detection rate after changes to achieve higher user satisfaction. Therefore, the model improves the redundancy and irrelevancy of requirements by more than 90% compared to other clustering methods and the analytical hierarchical process, achieving an 80% fault detection rate at an earlier stage. Hence, it provides guidelines for practitioners and researchers in the modern era. In the future, we will provide the working prototype of this model for proof of concept.

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