Few-shot prompting improves syntactic validity of LLM-generated code across ATL, ETL, QVTo, and Reactions, but semantic correctness gains remain uneven and language-dependent.
Exploring the potential of large language models in self-adaptive systems
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This paper proposes a research agenda for software engineering of self-adaptive robotic systems along lifecycle stages and enabling technologies, identifying challenges and a roadmap to 2030.
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LLM4MTLs: Automated Generation and Empirical Evaluation of Model Transformation Languages
Few-shot prompting improves syntactic validity of LLM-generated code across ATL, ETL, QVTo, and Reactions, but semantic correctness gains remain uneven and language-dependent.
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Software Engineering for Self-Adaptive Robotics: A Research Agenda
This paper proposes a research agenda for software engineering of self-adaptive robotic systems along lifecycle stages and enabling technologies, identifying challenges and a roadmap to 2030.