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A Systematic Literature Review on Effort Estimation in Agile Software Development using Machine Learning Techniques

by Pranay Tandon, Ugrasen Suman, Maya Rathore
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 184 - Number 21
Year of Publication: 2022
Authors: Pranay Tandon, Ugrasen Suman, Maya Rathore
10.5120/ijca2022922238

Pranay Tandon, Ugrasen Suman, Maya Rathore . A Systematic Literature Review on Effort Estimation in Agile Software Development using Machine Learning Techniques. International Journal of Computer Applications. 184, 21 ( Jul 2022), 15-23. DOI=10.5120/ijca2022922238

@article{ 10.5120/ijca2022922238,
author = { Pranay Tandon, Ugrasen Suman, Maya Rathore },
title = { A Systematic Literature Review on Effort Estimation in Agile Software Development using Machine Learning Techniques },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2022 },
volume = { 184 },
number = { 21 },
month = { Jul },
year = { 2022 },
issn = { 0975-8887 },
pages = { 15-23 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume184/number21/32440-2022922238/ },
doi = { 10.5120/ijca2022922238 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:22:03.118105+05:30
%A Pranay Tandon
%A Ugrasen Suman
%A Maya Rathore
%T A Systematic Literature Review on Effort Estimation in Agile Software Development using Machine Learning Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 184
%N 21
%P 15-23
%D 2022
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Agile software development is a way of frequent or continuous delivery of software. Nowadays many software industries have adopted agile for software development. The predictability and stability of traditional methods were replaced with flexibility, adaptability and agility to generate maximum value with collaboration and interaction, as quickly as possible. Effort estimation is the focused area in agile software development to achieve customer collaboration, respond to change and deliver a working software on time. Machine learning is an advanced tool to obtain effort estimation with available project data and widely used in IT industries to get accurate estimations. In this paper, the findings are reported through systematic literature review that aimed at identifying the applicability, limitations and individual result of most used machine learning techniques for effort estimation in agile software development with the help of 3 research questions. Also, suggested attributes of a robust machine learning model are discussed to achieve more accurate effort estimation. Conclusion of paper can help researchers and IT consultants in building a ML model considering the applicability, results and limitations of ML techniques.

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Index Terms

Computer Science
Information Sciences

Keywords

Agile software development effort estimation machine learning systematic literature review techniques methods limitations model deep learning.

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