Machine Learning in Software Development Life Cycle: A Comprehensive Review

Maryam Navaei; Nasseh Tabrizi

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Paper

Machine Learning in Software Development Life Cycle: A Comprehensive Review

Topics: Big Data for SSE; Interdisciplinary SSE ; Maintainability; Metrics; Software and System Testing

In Proceedings of the 17th International Conference on Evaluation of Novel Approaches to Software Engineering ENASE - Volume 1, 344-354, 2022

Authors: Maryam Navaei and Nasseh Tabrizi

Affiliation: Department of Computer Science, East Carolina University, East 5th Street, 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 2015-2021 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.

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Paper citation in several formats:

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 - ENASE; ISBN 978-989-758-568-5; ISSN 2184-4895, SciTePress, pages 344-354. DOI: 10.5220/0011040600003176

@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 - ENASE},
year={2022},
pages={344-354},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011040600003176},
isbn={978-989-758-568-5},
issn={2184-4895},
}

TY - CONF

JO - Proceedings of the 17th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE
TI - Machine Learning in Software Development Life Cycle: A Comprehensive Review
SN - 978-989-758-568-5
IS - 2184-4895
AU - Navaei, M.
AU - Tabrizi, N.
PY - 2022
SP - 344
EP - 354
DO - 10.5220/0011040600003176
PB - SciTePress