Publications

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.

Over 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.

Numerous language workbenches have been proposed over the past decade to ease the definition of Domain-Specific Languages (DSLs).Language workbenches enable DSL designers to specify DSLs using high-level metalanguages, and to automatically generate their implementation (e.g., parsers, interpreters) and tool support (e.g., editors, debuggers). 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 DSL designers to handcraft ad-hoc optimizations in interpreter implementations, or lose compatibility with tool support. In this paper, we propose to systematically exploit domain-specific information of DSL specifications to derive optimized Truffle-based language interpreters executed over the GraalVM. Those optimizations are provided at no extra cost for the DSL designer. They are of course not as efficient as handcrafted optimizations, but do not require extra time or knowledge from the DSL designer (which industrial DSL designers often lack). We implement our approach on top of the Eclipse Modeling Framework (EMF) by complementing its existing compilation chain with Truffle-specific information, which drives GraalVM to benefit from optimized just-in-time compilation. A key benefit of our approach is that it leverages existing DSL specifications and does not require additional information from DSL 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 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.