Towards Self-Adaptable Languages

International audienceOver recent years, self-adaptation has become a concern for many software systems that have to operate in complex and changing environments. At the core of self-adaptation, there is a feedback loop and associated trade-off reasoning to decide on the best course of action. However, existing software languages do not abstract the development and execution of such feedback loops for self-adaptable systems. Developers have to fall back to ad-hoc solutions to implement self-adaptable systems, often with wide-ranging design implications (e.g., explicit MAPE-K loop). Furthermore, existing software languages do not capitalize on monitored usage data of a language and its modeling environment. This hinders the continuous and automatic evolution of a software language based on feedback loops from the modeling environment and runtime software system. To address the aforementioned issues, this paper introduces the concept of Self-Adaptable Language (SAL) to abstract the feedback loops at both system and language levels. We propose L-MODA (Language, Models, and Data) as a conceptual reference framework that characterizes the possible feedback loops abstracted into a SAL. To demonstrate SALs, we present emerging results on the abstraction of the system feedback loop into the language semantics. We report on the concept of Self-Adaptable Virtual Machines as an example of semantic adaptation in a language interpreter and present a roadmap for SALs

Villageois et autorités locales dans un pays de vignoble : l'exemple de quelques communautés rurales de la région parisienne (1750- 1790)

Jouneaux Olivier. Villageois et autorités locales dans un pays de vignoble : l'exemple de quelques communautés rurales de la région parisienne (1750- 1790). In: Annales de Bretagne et des pays de l'Ouest. Tome 100, numéro 4, 1993. La culture paysanne (1750-1830) pp. 533-544

SEALS: A framework for building Self-Adaptive Virtual Machines

International audienceOver 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, selfadaptation 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 domainspecific 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

Towards Self-Adaptable Languages

International audienceOver recent years, self-adaptation has become a concern for many software systems that have to operate in complex and changing environments. At the core of self-adaptation, there is a feedback loop and associated trade-off reasoning to decide on the best course of action. However, existing software languages do not abstract the development and execution of such feedback loops for self-adaptable systems. Developers have to fall back to ad-hoc solutions to implement self-adaptable systems, often with wide-ranging design implications (e.g., explicit MAPE-K loop). Furthermore, existing software languages do not capitalize on monitored usage data of a language and its modeling environment. This hinders the continuous and automatic evolution of a software language based on feedback loops from the modeling environment and runtime software system. To address the aforementioned issues, this paper introduces the concept of Self-Adaptable Language (SAL) to abstract the feedback loops at both system and language levels. We propose L-MODA (Language, Models, and Data) as a conceptual reference framework that characterizes the possible feedback loops abstracted into a SAL. To demonstrate SALs, we present emerging results on the abstraction of the system feedback loop into the language semantics. We report on the concept of Self-Adaptable Virtual Machines as an example of semantic adaptation in a language interpreter and present a roadmap for SALs

Automatic generation of Truffle-based interpreters for Domain-Specific Languages

International audienceNumerous language workbenches have been proposed over the past decade to ease the definition of Domain-Specific Languages (DSLs). Language workbenches enable language designers to specify DSLs using high-level metalanguages and to generate their implementation (e.g., parsers, interpreters) and tool support (e.g., editors, debuggers) automatically. However, little attention has been given to the performance of the resulting interpreters. In many domains where performance is key (e.g., scientific and high-performance computing), this forces language designer to hand-craft ad-hoc optimizations in the interpreter implementations, or to lose compatibility with tool support. In this paper, we propose to systematically exploit the domain-specific information of language specifications to derive optimized Truffle-based language interpreters executed over the GraalVM. We implement our approach on top of the Eclipse Modeling Framework (EMF) by complementing its existing compilation chain with Truffle-specific information, which drives the GraalVM to benefit from an optimized just-in-time compilation. A key benefit of our approach is that it leverages existing language specifications and does not require additional information from language designers who remain oblivious of Truffle's low-level intricacies and JIT optimizations in general while staying compatible with tool support. We evaluate our approach using a representative set of four DSLs and eight conforming programs. Compared to the standard interpreters generated by EMF running on the GraalVM, we observe an average speed-up of x1.14, ranging from x1.07 to x1.26. Although the benefits vary slightly from one DSL or program to another, we conclude that our approach yields substantial performance gains while remaining non-intrusive of EMF abstractions

Adaptive Structural Operational Semantics

International audienceSoftware 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 selfadaptable behavior. We demonstrate, for RobLANG, that our approach provides suitable abstractions for specifying sound adaptive operational semantics while being more efficient