The Abstract State Machine (ASM) method proposes the concept of ground models for analyzing a target system based on pseudo-code-like descriptions for reasoning about system properties in terms of state machine runs over abstract data structures. This highly iterative process builds on stepwise refinement of ground models that evolve with progressing understanding of functional system requirements. Usually, as complexity increases, reorganization of a model's internal structure helps enhance its flexibility and robustness. While this approach is common practice, the underlying principles are usually left implicit. In this paper, we propose refactoring patterns to restructure abstract machine models with the goal of improving their intelligibility and maintainability. © 2012 Springer-Verlag.
CITATION STYLE
Yaghoubi Shahir, H., Farahbod, R., & Glässer, U. (2012). Refactoring abstract state machine models. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7316 LNCS, pp. 345–348). https://doi.org/10.1007/978-3-642-30885-7_28
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