Abstract Domain-specific languages (DSLs) promise to improve the software engineering process, e.g., by reducing software development and maintenance effort and by improving communication, and are therefore seeing increased use in industry. To support the creation and deployment of DSLs, language workbenches have been developed. However, little is published about the actual added value of a language workbench in an industrial setting, compared to not using a language workbench. In this paper, we evaluate the productivity of using the Spoofax language workbench by comparing two implementations of an industrial DSL, one in Spoofax and one in Python, that already existed before the evaluation. The subject is the Open Interaction Language (OIL): a complex DSL for implementing control software with requirements imposed by its industrial context at Canon Production Printing. Our findings indicate that it is more productive to implement OIL using Spoofax compared to using Python, especially if editor services are desired. Although Spoofax was sufficient to implement OIL, we find that Spoofax should especially improve on practical aspects to increase its adoptability in industry.

Abstract The proprietary State Machine Modelling Tool (SMMT), developed and maintained at Canon Production Printing, can be used to model software components using state machines and generate executable production code. We provide an operational semantics of the language supported by SMMT, derived from already existing code generators and discussions with engineers. By subsequently formalising this operational semantics in the mCRL2 language, we unlock the ability to apply formal verification to SMMT models during their design using the mCRL2 toolset. Using the mCRL2 formalisation, we have found various subtle bugs in the implementation of the SMMT tool, affecting its correctness, and proposed fixes for SMMT.

Abstract To aid in making software bug-free, several high-tech companies are moving from coding to modelling. In some cases model checking techniques are explored or have already been adopted to get more value from these models. This also holds for Canon Production Printing, where the language OIL was developed for modelling control-software components. In this paper, we present OIL and give its semantics. We define a translation from OIL to mCRL2 to enable the use of model checking techniques. Moreover, we discuss validity requirements on OIL component specifications and show how these can be formalised and verified using model checking. To test the feasibility of these techniques, we apply them to two models of systems used in production.

Reasoning about the correctness of parallel and distributed systems requires automated tools. By now, the mCRL2 toolset and language have been developed over a course of more than fifteen years. In this paper, we report on the progress and advancements over the past six years. Firstly, the mCRL2 language has been extended to support the modelling of probabilistic behaviour. Furthermore, the usability has been improved with the addition of refinement checking, counterexample generation and a user-friendly GUI. Finally, several performance improvements have been made in the treatment of behavioural equivalences. Besides the changes to the toolset itself, we cover recent applications of mCRL2 in software product line engineering and the use of domain specific languages (DSLs).