Visualizing Multi-dimensional State Spaces Using Selective Abstraction

Abstract

Domain-specific languages (DSLs) are popular for many reasons, such as increasing productivity for developers and improving communication with domain experts. Both textual and graphical DSLs are viable solutions with complementary pros and cons: while graphical DSLs shorten the learning curve and facilitate documentation and communication, textual DSLs aim at higher productivity thanks to more efficient editor functionalities. This paper presents the industrial experience on the adoption of a hybrid approach combining an existing textual DSL with a readonly graphical state machine representation (visualization), equipped with a selective abstraction functionality that offers user-specific, highly configurable views on states and transitions. Our approach is the result of an evolutionary process to improve the modelling experience, relying on frequent user feedback. We argue that a well-tailored visualization is a suitable way to shorten the learning curve and ease the adoption of model-driven approaches in industrial settings.

Giving a Model-Based Testing Language a Formal Semantics via Partial MAX-SAT

“…A graph-based representation of the modeled state machine -e.g. in the form of a Moore Machine, would have a beneficial impact on further stages of our MBT toolchain, e.g test case generation [23], visualization [7] and tracing. A Partial Moore Machine generated from the MDML model in Listing 1.1 can be seen in Figure 1.…”