Large Language Models for Software Engineering and Software Applications

Dear Colleagues,

The rapid advancements in Machine Learning (ML) have had a profound impact on various domains, including software engineering (SE). This Special Issue, entitled "Large Language Models for Software Engineering and Software Applications", aims to explore the integration of ML techniques in SE, with a particular emphasis on the transformative potential of Large Language Models (LLMs).

ML, and especially LLMs, have demonstrated remarkable capabilities in addressing complex tasks through extensive training on massive textual corpora. In SE, these models have shown promising results in activities such as code generation, code comprehension, test generation, and program repair. Despite their significant progress, there remains vast potential to further leverage ML and LLMs for solving code-relevant problems.

Key topics of interest for this Special Issue include the following:

• Novel approaches to applying ML, including LLMs, for solving code-relevant tasks.
• Designing and improving ML models, with a focus on LLMs, specifically for SE applications.
• Developing robust benchmarks and evaluation metrics for ML and LLMs in code-related tasks.

This Special Issue aims to achieve several goals: facilitating the exchange of cutting-edge research and preliminary findings, addressing open challenges and identifying future research directions, and encouraging the sharing of foundational infrastructures and benchmarks. By incorporating diverse formats such as research articles, case studies, and empirical evaluations, this Special Issue will provide a rich forum for both academic and industrial participants to discuss and advance the state of the art in ML for SE.

We invite contributions that address the following:

• Open research problems and innovative solutions in ML for SE.
• New techniques and tools leveraging ML, particularly LLMs, for SE tasks.
• Benchmarks and datasets essential for advancing ML and LLM research in SE.
• Empirical studies evaluating the effectiveness of ML and LLMs in SE applications.

Through this Special Issue, we aim to foster collaboration and drive forward the integration of ML, particularly LLMs, in the field of software engineering, ultimately enhancing the efficiency and effectiveness of software development processes.

Dr. Yuekang Li
Dr. Kailong Wang
Dr. Weisong Sun
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• machine learning
• deep learning
• large language models
• software engineering
• software application
• mlops