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A domain specific language (DSL) abstracts from implementation details and is aligned with the way domain experts reason about a software component. The development of DSLs is usually centered around a grammar and transformations that generate implementation code or analysis models. The semantics of the language is often defined implicitly and in terms of a transformation to implementation code. In the presence of multiple transformations from the DSL, the correctness of the generated artifacts with respect to the semantics of the DSL is a relevant issue. We show that a formal semantics is essential for checking the correctness of the generated artifacts. We exploit the formal semantics in an industrial project and use formal techniques based on equivalence checking and model-based testing for validating the correctness of the generated artifacts. We report about our experience with this approach in an industrial development project., Comment: In Proceedings FESCA 2016, arXiv:1603.08371

A domain specific language (DSL) abstracts from implementation details and is aligned with the way domain experts reason about a software component. The development of DSLs is usually centered around a grammar and transformations that generate implementation code or analysis models. The semantics of the language is often defined implicitly and in terms of a transformation to implementation code. In the presence of multiple transformations from the DSL, the consistency of the generated artifacts with respect to the semantics of the DSL is a relevant issue. We show that a formal semantics is essential for checking the consistency between the generated artifacts. We exploit the formal semantics in an industrial project and use formal techniques based on equivalence checking and model-based testing for consistency checking. We report about our experience with this approach in an industrial development project.

In this article, we consider a simple representation for real numbers and propose top-down procedures to approximate various algebraic and transcendental operations with arbitrary precision. Detailed algorithms and proofs are provided to guarantee the correctness of the approximations. Moreover, we develop and apply a perturbation analysis method to show that our approximation procedures only recompute expressions when unavoidable. In the last decade, various theories have been developed and implemented to realize real computations with arbitrary precision. Proof of correctness for existing approaches typically consider basic algebraic operations, whereas detailed arguments about transcendental operations are not available. Another important observation is that in each approach some expressions might require iterative computations to guarantee the desired precision. However, no formal reasoning is provided to prove that such iterative calculations are essential in the approximation procedures. In our approximations of real functions, we explicitly relate the precision of the inputs to the guaranteed precision of the output, provide full proofs and a precise analysis of the necessity of iterations.