A framework for automated quality assessment of software requirement specification based on part-of-speech tagging, multi-agent k-means clustering and case-based reasoning

Software Requirement Specification (SRS) is an imperative process in a Software Engineering (SE) cycle, where its role is to document functional and non-functional requirements and to establish the tasks that a particular system is set to accomplish. Because a badly written SRS has an expensive impa...

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Main Author:	Jubair, Mohammed Ahmed
Format:	Thesis
Language:	English English English
Published:	2022
Subjects:	T Technology (General)
Online Access:	http://eprints.uthm.edu.my/8438/1/24p%20MOHAMMED%20AHMED%20JUBAIR.pdf http://eprints.uthm.edu.my/8438/2/MOHAMMED%20AHMED%20JUBAIR%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/8438/3/MOHAMMED%20AHMED%20JUBAIR%20WATERMARK.pdf
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spelling	my-uthm-ep.84382023-02-26T07:52:27Z A framework for automated quality assessment of software requirement specification based on part-of-speech tagging, multi-agent k-means clustering and case-based reasoning 2022-01 Jubair, Mohammed Ahmed T Technology (General) Software Requirement Specification (SRS) is an imperative process in a Software Engineering (SE) cycle, where its role is to document functional and non-functional requirements and to establish the tasks that a particular system is set to accomplish. Because a badly written SRS has an expensive impact on the entire project, the success or failure of any software product depends on the quality of the SRS document. Recent advancements in the field have explored automated extraction of quality attributes in SRS documents such as the Reconstructed ARM and the Rendex models. However, automating the quality assessment process poses major challenges, which requires advanced Natural Language Processing (NLP) algorithms to extract the quality features, interpreting the context of the features, formulating the assessment metrics, and documenting the shortcomings as well as possible improvements. Recent automated models also attempted to assess the quality of the SRS based on a small number of quality attributes and indicators due to the limitation in extracting quality attributes that require specific indicators from the SRS. To address this gap, this thesis proposes an Automated Quality Assessment of SRS (AQA-SRS) framework by integrating NLP for feature extraction, Multi-Agent System (MAS) with K-means for features clustering, and Case-based Reasoning (CBR) for process management. This framework assessed the SRS documents by automatically extracted 11 quality attributes and their corresponding 11 quality indicators through a deep analysis of the SRS textual content. This process is performed through the Multi-Agent K-means (MA-K-means) model for handling the automatic evaluation of the AQA-SRS framework. The performance of the AQA-SRS framework is evaluated by comparing the results against the state-of-the-art techniques as well as human experts based on two standard SRS datasets. The results showed the AQA-SRS framework reliably handled the assessment of 11 quality attributes and their corresponding 11 quality indicators with Krippendorff’s Alpha 0.78 for the agreement with software engineering experts. 2022-01 Thesis http://eprints.uthm.edu.my/8438/ http://eprints.uthm.edu.my/8438/1/24p%20MOHAMMED%20AHMED%20JUBAIR.pdf text en public http://eprints.uthm.edu.my/8438/2/MOHAMMED%20AHMED%20JUBAIR%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/8438/3/MOHAMMED%20AHMED%20JUBAIR%20WATERMARK.pdf text en validuser phd doctoral Universiti Tun Hussein Onn Malaysia Fakulti Sains Komputer dan Teknologi Maklumat
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	T Technology (General)
spellingShingle	T Technology (General) Jubair, Mohammed Ahmed A framework for automated quality assessment of software requirement specification based on part-of-speech tagging, multi-agent k-means clustering and case-based reasoning
description	Software Requirement Specification (SRS) is an imperative process in a Software Engineering (SE) cycle, where its role is to document functional and non-functional requirements and to establish the tasks that a particular system is set to accomplish. Because a badly written SRS has an expensive impact on the entire project, the success or failure of any software product depends on the quality of the SRS document. Recent advancements in the field have explored automated extraction of quality attributes in SRS documents such as the Reconstructed ARM and the Rendex models. However, automating the quality assessment process poses major challenges, which requires advanced Natural Language Processing (NLP) algorithms to extract the quality features, interpreting the context of the features, formulating the assessment metrics, and documenting the shortcomings as well as possible improvements. Recent automated models also attempted to assess the quality of the SRS based on a small number of quality attributes and indicators due to the limitation in extracting quality attributes that require specific indicators from the SRS. To address this gap, this thesis proposes an Automated Quality Assessment of SRS (AQA-SRS) framework by integrating NLP for feature extraction, Multi-Agent System (MAS) with K-means for features clustering, and Case-based Reasoning (CBR) for process management. This framework assessed the SRS documents by automatically extracted 11 quality attributes and their corresponding 11 quality indicators through a deep analysis of the SRS textual content. This process is performed through the Multi-Agent K-means (MA-K-means) model for handling the automatic evaluation of the AQA-SRS framework. The performance of the AQA-SRS framework is evaluated by comparing the results against the state-of-the-art techniques as well as human experts based on two standard SRS datasets. The results showed the AQA-SRS framework reliably handled the assessment of 11 quality attributes and their corresponding 11 quality indicators with Krippendorff’s Alpha 0.78 for the agreement with software engineering experts.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Jubair, Mohammed Ahmed
author_facet	Jubair, Mohammed Ahmed
author_sort	Jubair, Mohammed Ahmed
title	A framework for automated quality assessment of software requirement specification based on part-of-speech tagging, multi-agent k-means clustering and case-based reasoning
title_short	A framework for automated quality assessment of software requirement specification based on part-of-speech tagging, multi-agent k-means clustering and case-based reasoning
title_full	A framework for automated quality assessment of software requirement specification based on part-of-speech tagging, multi-agent k-means clustering and case-based reasoning
title_fullStr	A framework for automated quality assessment of software requirement specification based on part-of-speech tagging, multi-agent k-means clustering and case-based reasoning
title_full_unstemmed	A framework for automated quality assessment of software requirement specification based on part-of-speech tagging, multi-agent k-means clustering and case-based reasoning
title_sort	framework for automated quality assessment of software requirement specification based on part-of-speech tagging, multi-agent k-means clustering and case-based reasoning
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Fakulti Sains Komputer dan Teknologi Maklumat
publishDate	2022
url	http://eprints.uthm.edu.my/8438/1/24p%20MOHAMMED%20AHMED%20JUBAIR.pdf http://eprints.uthm.edu.my/8438/2/MOHAMMED%20AHMED%20JUBAIR%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/8438/3/MOHAMMED%20AHMED%20JUBAIR%20WATERMARK.pdf
_version_	1776103345826562048

A framework for automated quality assessment of software requirement specification based on part-of-speech tagging, multi-agent k-means clustering and case-based reasoning

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