Call for papers

As software systems continue to grow in complexity and scale, traditional software engineering approaches face increasing challenges in design, implementation, maintenance, and evolution. The emergence of powerful Generative AI technologies, particularly Large Language Models (LLMs), has created unprecedented opportunities to re-imagine how software is developed and maintained. Simultaneously, multi-agent systems offer established frameworks for distributed intelligence, collaboration, and autonomous problem-solving that can effectively address many of the inherent challenges in modern software engineering. The convergence of these two paradigms (i.e., generative AI capabilities and multi-agent architectures) presents a promising new frontier for automated software engineering.

In such a context, this workshop offers a unique platform to bring together leading researchers, industry practitioners, and visionary experts to explore the integration of Multi-Agent Systems (MAS), Generative Artificial INtelligence (GAIN), and automated software engineering. We aim to foster interdisciplinary discussions and spark innovative ideas that push the boundaries of current technology. In an era where complex software systems demand more adaptive, resilient, and autonomous solutions, multi-agent architecture provides a powerful paradigm for distributed problem-solving and collaborative decision-making. At the same time, breakthroughs in generative artificial intelligence, most notably with LLMs, are enabling unprecedented capabilities in automating the design, development, and maintenance of software systems. These advances not only streamline coding and testing processes but also open new avenues for creative systems and software engineering.

The MAS-GAIN 2025 provides a forum for researchers and practitioners to propose and discuss these advancements, addressing both theoretical foundations and practical implementations. Participants are encouraged to share research findings, case studies, and emerging trends that demonstrate how the synergy between MAS, GAIN (including LLM-driven approaches), and automated software engineering can lead to smarter, more efficient, and scalable software engineering practices.

By bridging these domains, the workshop aims to lay the groundwork for next-generation software solutions that are both intelligent and autonomous.

Topics of interest include, but are not limited to, the following:

• MAS for software engineering powered by generative AI, orchestration, and novel frameworks for MAS coordination
• Application of MAS for code synthesis, system modeling, and overall software engineering.
• Integration of heterogeneous models in multi-agent platforms
• Development of visual user interfaces and low/no-code support
• Optimization strategies concerning agent energy consumption and resource usage
• Research on local deployment and dockerization of MAS
• Recommendations of agents based on tasks or characteristics
• Integration of agent frameworks with development environments and software engineering tools
• Ethical considerations in AI-driven automated software engineering
• Verification and validation of AI-generated code and artifacts
• Human-agent collaboration
• Agent-based approaches to software maintenance and evolution
• Industrial applications, case studies, and empirical evaluations of MAS

Please note that surveys, (systematic) literature reviews, and mapping studies are out of the scope of this workshop and will be desk-rejected.

Submission process

MAS-GAIN 2025 welcomes research papers, experience papers, and tool presentations; nevertheless, papers describing novel research contributions and innovative applications are of particular interest. Contribution can be:

Regular papers (up to 8 pages, including references): in this category fall those contributions that propose novel research contributions, address challenging problems with innovative ideas, or offer practical contributions (e.g., industrial experiences and case studies) in the intersection of Multi-Agent Systems (MAS) and Generative Artificial Intelligence (GAIN), particularly Large Language Models (LLMs), for advancing automated software engineering. Regular papers should clearly describe the situation or problem tackled, the relevant state of the art, the position or solution suggested, and the potential benefits of the contribution. Authors of papers reporting industrial experiences are strongly encouraged to make their experimental results available for use by reviewers. Similarly, case-study papers should describe significant case studies, and the complete development should be made available for use by reviewers.

Short papers (up to 4 pages, including references): this category includes tool demonstrations, position papers, well-pondered and sufficiently documented visionary papers. Tool demonstration papers should explain enhancements made in comparison to previously published work. Authors of demonstration papers should make their tool available for use by reviewers.

All papers must:

• be written in English;
• be in PDF format and conform, at time of submission, to the IEEE Conference Proceedings Formatting Guidelines (title in 24pt font and full text in 10pt type, LaTeX users must use \documentclass[10pt,conference]{IEEEtran} without including the compsoc or compsocconf option);
• not exceed 4 pages (short papers) or 8 pages (regular papers), including references.

Submissions are required to report on original, unpublished work and should not be submitted simultaneously for publication elsewhere.

Each submitted paper will undergo a formal peer review process by at least 3 Program Committee members.

Accepted papers will be included in the ASE's conference proceedings.

If a submission is accepted, at least one author of the paper is required to register for MAS-GAIN 2025 and present the paper.

Paper submission is done via EasyChair.

Submission site