State Machine Flattening: Mapping Study and Assessment
This incremental study identifies gaps in flattening techniques and tools for model-based behavioral development in software engineering.
The researchers conducted a systematic mapping study of 30 publications to assess the importance of flattening techniques in transforming hierarchical and parallel state machine models into executable code or test inputs, finding that flattening is rarely the primary focus and tool support is limited in scalability.
State machine formalisms equipped with hierarchy and parallelism allow to compactly model complex system behaviours. Such models can then be transformed into executable code or inputs for model-based testing and verification techniques. Generated artifacts are mostly flat descriptions of system behaviour. \emph{Flattening} is thus an essential step of these transformations. To assess the importance of flattening, we have defined and applied a systematic mapping process and 30 publications were finally selected. However, it appeared that flattening is rarely the sole focus of the publications and that care devoted to the description and validation of flattening techniques varies greatly. Preliminary assessment of associated tool support indicated limited tool availability and scalability on challenging models. We see this initial investigation as a first step towards generic flattening techniques and scalable tool support, cornerstones of reliable model-based behavioural development.