The stakeholders involved in software development are becoming increasingly diverse, with both human contributors from varied backgrounds and AI-powered agents collaborating together in the process. This situation presents unique governance challenges, particularly in Open-Source Software (OSS) projects, where explicit policies are often lacking or unclear. This paper presents the vision and foundational concepts for a novel Domain-Specific Language (DSL) designed to define and enforce rich governance policies in systems involving diverse stakeholders, including agents. This DSL offers a pathway towards more robust, adaptable, and ultimately automated governance, paving the way for more effective collaboration in software projects, especially OSS ones.

Software systems evolve more and more in complex and changing environments, often requiring runtime adaptation to best deliver their services. When self-adaptation is the main concern of the system, a manual implementation of the underlying feedback loop and trade-off analysis may be desirable. However, the required expertise and substantial development effort make such implementations prohibitively difficult when it is only a secondary concern for the given domain. In this paper, we present ASOS, a metalanguage abstracting the runtime adaptation concern of a given domain in the behavioral semantics of a domain-specific language (DSL), freeing the language user from implementing it from scratch for each system in the domain. We demonstrate our approach on RobLANG, a procedural DSL for robotics, where we abstract a recurrent energy-saving behavior depending on the context. We provide formal semantics for ASOS and pave the way for checking properties such as determinism, completeness, and termination of the resulting self-adaptable language. We provide first results on the performance of our approach compared to a manual implementation of this self-adaptable behavior. We demonstrate, for RobLANG, that our approach provides suitable abstractions for specifying sound adaptive operational semantics while being more efficient.

Over recent years, self-adaptation has become a major concern for software systems that evolve in changing environments. While expert developers may choose a manual implementation when self-adaptation is the primary concern, self-adaptation should be abstracted for non-expert developers or when it is a secondary concern. We present SEALS, a framework for building self-adaptive virtual machines for domain-specific languages. This framework provides first-class entities for the language engineer to promote domain-specific feedback loops in the definition of the DSL operational semantics. In particular, the framework supports the definition of (i) the abstract syntax and the semantics of the language as well as the correctness envelope defining the acceptable semantics for a domain concept, (ii) the feedback loop and associated trade-off reasoning, and (iii) the adaptations and the predictive model of their impact on the trade-off. We use this framework to build three languages with self-adaptive virtual machines and discuss the relevance of the abstractions, effectiveness of correctness envelopes, and compare their code size and performance results to their manually implemented counterparts. We show that the framework provides suitable abstractions for the implementation of self-adaptive operational semantics while introducing little performance overhead compared to a manual implementation.