Open Access

ARTICLE

Requirements Engineering: Conflict Detection Automation Using Machine Learning

by Hatim Elhassan^1,*, Mohammed Abaker¹, Abdelzahir Abdelmaboud², Mohammed Burhanur Rehman¹

1 Department of Computer Science, College of Applied Sciences, King Khalid University, Muhayil, 63772, Saudi Arabia
2 Department of Information System, College of Science, King Khalid University, Muhayil, 63772, Saudi Arabia

* Corresponding Author:Mohammed Burhanur Rehman. Email:

Intelligent Automation & Soft Computing 2022, 33(1), 259-273. https://doi.org/10.32604/iasc.2022.023750

Received 20 September 2021; Accepted 12 November 2021; Issue published 05 January 2022

Abstract

The research community has well recognized the importance of requirement elicitation. Recent research has shown the continuous decreasing success rate of IS projects in the last five years due to the complexity of the requirement conflict refinement process. Requirement conflict is at the heart of requirement elicitation. It is also considered the prime reason for deciding the success or failure of the intended Information System (IS) project. This paper introduces the requirements conflict detection automation model based on the Mean shift clustering unsupervised machine learning model. It utilizes the advantages of Artificial Intelligence in detecting and classifying the requirement conflicts occurring in the requirement elicitation phase. An experiment of the proposed model was conducted, composed of 207 observations and 11 parameters. The results show that the correct detection accuracy for the (Conflicted Requirements, Partial Conflicted Requirements & Conflict Free Requirements). The proposed model findings provide a promising and effective detection process regarding requirements classification. The model validation process provides a performance comparison between the model output vs. the output produced by the requirement conflict verification phase, detailing the Standard Error (SE) measure of accuracy values and the detected clusters. The implications of this study could be used to promote the automatization of the requirement elicitation process. Thus, increasing the potentiality of enhancing the produced systems designs.

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Keywords

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