Impact of Machine Learning on Software Development Life Cycle

Maryam Navaei, Nasseh Tabrizi

2023

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 2015-2023 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. (2023). Impact of Machine Learning on Software Development Life Cycle. In Proceedings of the 18th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE, ISBN 978-989-758-647-7, SciTePress, pages 718-726. DOI: 10.5220/0011997200003464

in Bibtex Style

@conference{enase23,
author={Maryam Navaei and Nasseh Tabrizi},
title={Impact of Machine Learning on Software Development Life Cycle},
booktitle={Proceedings of the 18th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE,},
year={2023},
pages={718-726},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011997200003464},
isbn={978-989-758-647-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE,
TI - Impact of Machine Learning on Software Development Life Cycle
SN - 978-989-758-647-7
AU - Navaei M.
AU - Tabrizi N.
PY - 2023
SP - 718
EP - 726
DO - 10.5220/0011997200003464
PB - SciTePress