Authors : Dipali B. Tawar

Volume/Issue : Volume 9 - 2024, Issue 5 - May

Google Scholar : https://tinyurl.com/2krdv9c5

Scribd : https://tinyurl.com/2s4f5us7

DOI : https://doi.org/10.38124/ijisrt/IJISRT24MAY2519

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : In today's rapidly evolving technological era, the role of machine learning in software development has become increasingly vital and influential. Machine learning has revolutionized various aspects of software development, from code analysis and optimization to prediction and decision-making. Moreover, machine learning algorithms have the potential to significantly enhance the software development process by automating repetitive tasks, improving code quality, and reducing the time and effort required for software testing and debugging. By gaining lots of data and powerful computing resources, machine learning algorithms can be able to analyse patterns and make accurate predictions about software performance, identify potential bugs or security issues, and assist in improving software design and development processes. Furthermore, machine learning can facilitate software maintenance and debugging by detecting anomalies and identifying potential causes of software failures. Albeit, using machine learning techniques into the software development process can greatly improve efficiency, productivity, and overall software quality. In this paper it is presenting the tools, techniques and the application of Machine Learning (ML) in different phases of Software Development Life Cycle (SDLC) for enhancing and improving the software development process.

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