R obust engineering methodologies offering product lifecycle control have proved to be a cornerstone in modern software development projects. Simultaneously, various modeling and simulation (M&S) techniques have become increasingly adopted in complex system design, particularly in scenarios in which it's difficult to predict system behavior as changes are introduced.The DEVS (Discrete Event Systems Specification) framework is the most general formalism for modeling discrete event systems 1-3 and has been adopted in several disciplines for complex software and hardware system design and analysis. 4,5 In addition to providing an unambiguous mathematical formalism to define model behavior and structure, DEVS provides a clear framework for system analysis, experimental frame definition, model-to-simulator verification, and model-to-system validation.We present a DEVS-based methodology for M&S-driven engineering projects that integrates software development best practices tailored to a large-scale networked data acquisition system in a physics experiment (specifically, the ATLAS particle detector 6 at CERN 7 ). This project poses M&S challenges from several viewpoints, including system complexity, tight delivery times, the quality and flexibility of the developed models and tools, interdisciplinary communication of results to collaborators (mostly scientists), and big data-scale analysis.
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