Machine Learning in Software Development Life Cycle: A Comprehensive Review

Maryam Navaei, Nasseh Tabrizi

2022

Abstract

This research concludes an overall summary of the publications so far on the applied Machine Learning (ML) techniques in different phases of Software Development Life Cycle (SDLC) that includes Requirement Analysis, Design, Implementation, Testing, and Maintenance. We have performed a systematic review of the research studies published from 2015-2021 and revealed that Software Requirements Analysis phase has the least number of papers published; in contrast, Software Testing is the phase with the greatest number of papers published.

Paper Citation

in Harvard Style

Navaei M. and Tabrizi N. (2022). Machine Learning in Software Development Life Cycle: A Comprehensive Review. In Proceedings of the 17th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE, ISBN 978-989-758-568-5, pages 344-354. DOI: 10.5220/0011040600003176

in Bibtex Style

@conference{enase22,
author={Maryam Navaei and Nasseh Tabrizi},
title={Machine Learning in Software Development Life Cycle: A Comprehensive Review},
booktitle={Proceedings of the 17th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE,},
year={2022},
pages={344-354},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011040600003176},
isbn={978-989-758-568-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE,
TI - Machine Learning in Software Development Life Cycle: A Comprehensive Review
SN - 978-989-758-568-5
AU - Navaei M.
AU - Tabrizi N.
PY - 2022
SP - 344
EP - 354
DO - 10.5220/0011040600003176