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Machine Learning for Software Engineering: Models, Methods, and Applications

Machine Learning for Software Engineering: Models, Methods, and Applications

Aman Kumar
Copyright: © 2024 |Pages: 5
ISBN13: 9798369335024|ISBN13 Softcover: 9798369348758|EISBN13: 9798369335031
DOI: 10.4018/979-8-3693-3502-4.ch007
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MLA

Kumar, Aman. "Machine Learning for Software Engineering: Models, Methods, and Applications." Advancing Software Engineering Through AI, Federated Learning, and Large Language Models, edited by Avinash Kumar Sharma, et al., IGI Global, 2024, pp. 105-109. https://doi.org/10.4018/979-8-3693-3502-4.ch007

APA

Kumar, A. (2024). Machine Learning for Software Engineering: Models, Methods, and Applications. In A. Sharma, N. Chanderwal, A. Prajapati, P. Singh, & M. Kansal (Eds.), Advancing Software Engineering Through AI, Federated Learning, and Large Language Models (pp. 105-109). IGI Global. https://doi.org/10.4018/979-8-3693-3502-4.ch007

Chicago

Kumar, Aman. "Machine Learning for Software Engineering: Models, Methods, and Applications." In Advancing Software Engineering Through AI, Federated Learning, and Large Language Models, edited by Avinash Kumar Sharma, et al., 105-109. Hershey, PA: IGI Global, 2024. https://doi.org/10.4018/979-8-3693-3502-4.ch007

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Abstract

Machine learning (ML) is a field of study that focuses on developing techniques to automatically derive models from data. Machine learning has shown effectiveness in various domains of software engineering, encompassing behaviors extraction, testing, and issue remediation. Several further applications have yet to be determined. Nevertheless, acquiring a more comprehensive comprehension of ML techniques, including their underlying assumptions and assurances, will facilitate the adoption and selection of suitable approaches by software developers for their intended applications. The authors contend that the selection can be influenced by the models one aims to deduce. This technical briefing examines and contemplates the utilization of machine learning in the field of software engineering, categorized based on the models they generate and the methodologies they employ.

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