Example-Based Sequence Diagrams to Colored Petri Nets Transformation Using Heuristic Search

Dynamic UML models like sequence diagrams (SD) lack sufficient formal semantics, making it difficult to build automated tools for their analysis, simulation and validation. A common approach to circumvent the problem is to map these models to more formal representations. In this context, many works propose a rule-based approach to automatically translate SD into colored Petri nets (CPN). However, finding the rules for such SD-to-CPN transformations may be difficult, as the transformation rules are sometimes difficult to define and the produced CPN may be subject to state explosion. We propose a solution that starts from the hypothesis that examples of good transformation traces of SD-to-CPN can be useful to generate the target model. To this end, we describe an automated SD-to-CPN transformation method which finds the combination of transformation fragments that best covers the SD model, using heuristic search in a base of examples. To achieve our goal, we combine two algorithms for global and local search, namely Particle Swarm Optimization (PSO) and Simulated Annealing (SA). Our empirical results show that the new approach allows deriving the sought CPNs with at least equal performance, in terms of size and correctness, to that obtained by a transformation rule-based tool.