Impact of Machine Learning on Software Development Life Cycle

Maryam Navaei; Nasseh Tabrizi

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Paper

Impact of Machine Learning on Software Development Life Cycle

Topics: Big Data for SSE; Empirical Software Engineering; Pattern Recognition and Machine Learning for SSE; Requirements Engineering; Software and System Testing; Software Development Methodologies; SSE for Data and Process Mining

In Proceedings of the 18th International Conference on Evaluation of Novel Approaches to Software Engineering ENASE - Volume 1, 718-726, 2023 , Prague, Czech Republic

Authors: Maryam Navaei and Nasseh Tabrizi

Affiliation: Department of Computer Science, East Carolina University, East 5th St., Greenville, NC, U.S.A.

Keyword(s): Software Engineering, Software Development Life Cycle, Artificial Intelligence, Machine Learning, Machine Learning Algorithms.

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.

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Paper citation in several formats:

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 - ENASE; ISBN 978-989-758-647-7; ISSN 2184-4895, SciTePress, pages 718-726. DOI: 10.5220/0011997200003464

@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 - ENASE},
year={2023},
pages={718-726},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011997200003464},
isbn={978-989-758-647-7},
issn={2184-4895},
}

TY - CONF

JO - Proceedings of the 18th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE
TI - Impact of Machine Learning on Software Development Life Cycle
SN - 978-989-758-647-7
IS - 2184-4895
AU - Navaei, M.
AU - Tabrizi, N.
PY - 2023
SP - 718
EP - 726
DO - 10.5220/0011997200003464
PB - SciTePress