The complexity and size of software systems has increased to the extent that traditional manual development and maintenance techniques are no longer adequate for the management of these systems. At the same time, the capabilities of machine learning (ML) systems to operate with code - to analyze, generate, and transform software - have increased to the level that specifically trained ML systems can effectively function as programming assistants to produce or improve code. There is therefore significant potential for utilizing ML approaches to address the problem of increasing software application complexity and scale.

This Research Topic will concern the application of AI techniques such as machine learning to software engineering: the application of AI techniques to accelerate software development and to improve software quality through specification and programming assistance, and to support software specification, design, implementation, maintenance, and related activities such as program translation and re-engineering for software modernization.

Submissions should address the theme of AI and ML assistance for software engineering processes, including topics such as:

* Machine learning approaches relevant for software engineering, including large language models (LLMs) and symbolic ML approaches such as program synthesis from examples.
* Natural language processing (NLP) and image processing techniques.

Papers should consider the practical application of ML and AI techniques to reduce manual workload and accelerate development for software processes such as:

- Requirements formalization
- Architectural design and the selection of architectural styles
- Software design and the selection of design patterns
- Test case construction and test suite optimization
- Software modeling and the construction of digital twins
- Program comprehension/documentation, program translation, and software reverse and re-engineering
- Low-code and no-code software development.

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Frontiers in Computer Science

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