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Mapping Software Requirements: An Overview of Classification Strategies

by Tamanna Yesmin Rashme
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International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 12 - Number 45
Year of Publication: 2024
Authors: Tamanna Yesmin Rashme
10.5120/ijais2024451976

Tamanna Yesmin Rashme . Mapping Software Requirements: An Overview of Classification Strategies. International Journal of Applied Information Systems. 12, 45 ( Jul 2024), 1-6. DOI=10.5120/ijais2024451976

@article{ 10.5120/ijais2024451976,
author = { Tamanna Yesmin Rashme },
title = { Mapping Software Requirements: An Overview of Classification Strategies },
journal = { International Journal of Applied Information Systems },
issue_date = { Jul 2024 },
volume = { 12 },
number = { 45 },
month = { Jul },
year = { 2024 },
issn = { 2249-0868 },
pages = { 1-6 },
numpages = {9},
url = { https://www.ijais.org/archives/volume12/number45/mapping-software-requirements-an-overview-of-classification-strategies/ },
doi = { 10.5120/ijais2024451976 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-07-18T00:40:46.279638+05:30
%A Tamanna Yesmin Rashme
%T Mapping Software Requirements: An Overview of Classification Strategies
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 12
%N 45
%P 1-6
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Effective software development relies on accurately identifying system requirements, which form the foundation for subsequent activities in the software life cycle (SLC). Proper classification of these requirements is crucial for subsequent design and implementation stages and can be achieved manually or through automated means. Machine Learning (ML) algorithms have emerged as valuable tools for addressing challenges in requirement engineering, such as identifying and prioritizing requirements. This paper examines ten studies to analyze the applied algorithms, presenting their training processes and evaluation metrics. The analysis aims to assist software engineers and researchers in selecting suitable requirement classification techniques by providing a detailed overview of various machine learning approaches and their effectiveness. Although the study references ten papers, it focuses on five key papers to demonstrate the results.

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Index Terms

Computer Science
Information Sciences

Keywords

SLC Software Requirement Classification Machine Learning.

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