discrete-event simulation

This study addresses the lack of systematic analysis of waterfall model applications in computational simulation practices within academic research. We conduct a systematic mapping study of literature published between 2000 and 2024. For the first time, we analyze studies along four dimensions: simulation methodology (e.g., discrete-event simulation, system dynamics), tooling platforms (e.g., Simphony.NET, SimPy), geographical distribution, and model fidelity. Our analysis identifies 68 empirical studies and reveals three key findings: (1) none fully replicate Royce’s original seven-stage waterfall model; (2) proprietary platforms are increasingly supplanted by open-source Python-based tools; and (3) although marginalized as a standalone paradigm, the waterfall model exhibits adaptive evolution within hybrid development approaches. The study elucidates its persistent influence and transformation pathways, establishing a novel methodological benchmark for simulation-based software engineering research.

Discrete system modeling in the digital era faces a foundational theoretical gap. This paper proposes Heraklit, a novel modeling framework that integrates philosophical inquiry with formal methods to establish a new abstraction paradigm for discrete systems. Departing from traditional continuity assumptions, Heraklit adopts dynamic flux as its ontological foundation and rigorously defines core concepts—including modeling correctness, the nature of information, and structural invariance. Its key contributions are threefold: (1) it formally axiomatizes Heraclitus’s “panta rhei” (“everything flows”) principle into a computationally tractable modeling foundation; (2) it establishes modeling as a fundamental discipline in the digital age; and (3) it provides an extensible theoretical architecture that unifies and enables future advances in modeling verification, semantic information modeling, and invariance analysis.

This study addresses the challenge of effectively validating input model specifications in digital twin simulations, where conventional approaches—relying solely on marginal output distributions—often fail to detect misspecified joint input models. To overcome this limitation, the authors propose a novel statistical validation framework based on sub-trajectory conditioning. By repeatedly restarting simulations from observed system states while conditioning on subsets of random inputs, the method constructs conditional output distributions that enable goodness-of-fit testing of the full joint input model. This approach innovatively transcends the constraints of marginal validation and is complemented by diagnostic tools to pinpoint specific input sources responsible for detected discrepancies. Empirical evaluations on M/M/1 and tandem queueing systems demonstrate the framework’s heightened sensitivity and effectiveness, successfully identifying input model misspecifications that traditional methods overlook.

Quantifying the frequency with which safety-critical properties hold across multiple simulation variants of hybrid programs—such as those in medical devices and autonomous vehicles—remains challenging. Method: This paper extends the Lince tool by introducing, for the first time, a parallel execution mechanism for multiple simulation variants and an attribute frequency statistical analysis module. Leveraging a C-style modeling language with native support for differential equations, the approach enables hybrid system modeling, integrates scheduling optimization, and automatically generates histograms representing the distribution of property satisfaction frequencies over large sets of simulation trajectories. Contributions/Results: (1) It proposes the first statistical verification framework tailored to hybrid programs with multiple variants; (2) it enables frequency-based verification of safety properties and identification of behavioral patterns; and (3) in an empirical evaluation on an adaptive cruise control system, it significantly enhances statistical interpretability under uncertainty and design variability.

This paper addresses discrete-time interconnected systems whose subsystem dynamics and interconnection topology are partially unknown. Method: We propose a data-driven, compositional approach to construct finite-state abstractions for formal verification and distributed controller synthesis. Subsystems are modeled individually from input-output data, and—novelly—the unknown static interconnection mapping is treated as a learnable object, enabling its symbolic abstraction. Compositionality and rigorous error propagation analysis ensure that the resulting abstraction strictly satisfies an approximate simulation relation. Contribution/Results: We theoretically establish scalability and verifiability of the abstraction. Experiments demonstrate substantial mitigation of the curse of dimensionality, enabling high-precision, low-complexity controller synthesis while preserving formal guarantees.

This work addresses the behavioral gap between formal verification and actual execution in traditional engineering approaches, which often neglect execution semantics. To bridge this semantic divide, the paper proposes a Modeling and Simulation-Based Engineering (MSBE) methodology that explicitly treats execution semantics as a first-class engineering entity. It defines executability as the admissible model space induced by the stabilization of execution conditions and unifies model behavior with physical execution through an iterative cycle of formal execution, experimental execution, verification, and activity-mediated validation. Integrating formal methods, simulation-based verification, activity theory, and constraint modeling, MSBE establishes a general-purpose engineering framework applicable to diverse cyber-physical systems (CPS). The approach demonstrates its generality and effectiveness across four CPS categories: human-centric, biophysical, technological, and digital twin systems.

This work addresses the challenges of transferability and computational feasibility in discrete abstraction for symbolic model checking of cyber-physical systems by proposing a conservatism-first, four-step modular workflow to construct finite-state abstractions of closed-loop dynamical systems. The approach integrates state partitioning, conservative transition construction, spurious behavior elimination, and specification semantics lifting, enabling composable and replaceable subroutine design. Transition relations are built using axis-aligned bounding boxes, polyhedra, and sampling with PAC coverage certificates, combined with certified erasure and counterexample-guided refinement. Reliable lifting of LTL specifications is achieved through may–must semantics. Evaluation across three case studies demonstrates that the workflow effectively balances abstraction accuracy and verification efficiency while clearly revealing the impact of different design choices on the outcomes.

Existing world models struggle to simultaneously ensure reliability, verifiability, and online adaptability in complex environments governed by discrete events. This work proposes an intermediate approach grounded in natural language specifications: leveraging large language models to iteratively generate discrete-event world models that conform to the DEVS (Discrete Event System Specification) formalism, thereby decoupling model structure from behavior. The generated models are validated against semantic and temporal constraints derived from structured event trajectories, ensuring consistency over long-horizon simulations. This framework supports observable behaviors, reproducible verification, localized diagnostics, and efficient online deployment, offering a principled pathway toward trustworthy and adaptive world modeling in discrete-event settings.

Existing approaches to verifying timed bisimulation for timed automata lack interpretability and are unable to generate witnesses or counterexamples. This work proposes a novel zone-based construction method grounded in an extended fictitious-clock semantics, integrated with compositional symbolic representations. For the first time, this approach enables the automatic generation of semantically valid witnesses in the case of equivalence, or concrete behavioral counterexamples demonstrating divergence when systems are inequivalent—all while preserving decision correctness. By unifying timed automata theory, symbolic model checking, and timed bisimulation algorithms, the method not only achieves efficient equivalence checking but also provides interpretable evidence to support system refinement, testing, and formal verification.