Software Engineering Framework for Software Defect Management Using Machine Learning Techniques with Azure

Subbiah, Uma; Ramachandran, Muthu; Mahmood, Zaigham

doi:10.1007/978-3-030-33624-0_7

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Applications Of ML Aiming At Software Maintenance1

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2021

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Cited by 2 publications

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“…In addition, there is a significant lack of studies showing the cost-benefit analysis of their proposed ML techniques, which would be vital for ML-based approaches to be feasible for adaptation in the industry. 190,191,192,193,194,195,196,197,198,199,200,201,202,203…”

Section: Applications Of ML Aiming At Software Maintenancementioning

confidence: 99%

A Literature Review of Using Machine Learning in Software Development Life Cycle Stages

Shafiq¹,

Mashkoor

Mayr-Dorn

et al. 2021

IEEE Access

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The software engineering community is rapidly adopting machine learning for transitioning modern-day software towards highly intelligent and self-learning systems. However, the software engineering community is still discovering new ways how machine learning can offer help for various software development life cycle stages. In this article, we present a study on the use of machine learning across various software development life cycle stages. The overall aim of this article is to investigate the relationship between software development life cycle stages, and machine learning tools, techniques, and types. We attempt a holistic investigation in part to answer the question of whether machine learning favors certain stages and/or certain techniques.

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