EDITORIAL article
Front. Comput. Sci.
Sec. Software
Volume 7 - 2025 | doi: 10.3389/fcomp.2025.1670939
This article is part of the Research TopicMachine Learning for Software EngineeringView all 7 articles
Editorial for Research Topic: Machine Learning for Software Engineering
Provisionally accepted
- 1King's College London, London, United Kingdom
- 2University of Roehampton, London, United Kingdom
- 3University College London, London, United Kingdom
- 4Princess Nourah bint Abdulrahman University College of Computer and Information Sciences, Riyadh, Saudi Arabia
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Artificial intelligence techniques such as machine learning (ML) and particularly deep learning (DL) tools such as large language models (LLMs) Zhao et al. (2023) Cursor being widely-used to produce program code based on natural language requirements and partial 27 specifications, such as the intended results of test cases. This approach is highly usable for practitioners, The six selected papers investigate different applications of AI and ML to software engineering, across a 47 range of domains. In Uandykova et al. (2025), the authors analyse the potential for LLM use in generating industrial-quality 49 Java code. They evaluate the ChatGPT LLM on a range of realistic tasks. They identify that the LLM can 50 improve developer productivity, and help to efficiently allocate human resources in projects. However, the 51 study also points out limitations of the LLM for solving complex problems, and for the need for human 52 intervention to verify, correct, and improve generated code.
Keywords: software engineering, artificial intelligence, machine learning, LLMs Lano al. Editorial for Research Topic: Machine Learning for Software Engineering, LLM
Received: 22 Jul 2025; Accepted: 01 Aug 2025.
Copyright: © 2025 Lano, Rahimi, Y. Tehrani and Alfraihi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Kevin Lano, King's College London, London, United Kingdom
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