design state machines

Consistency gaps frequently arise between implementations and formal specifications in Actor-model-based distributed systems. Method: This paper proposes a non-intrusive model-based testing approach: it models system behavior using finite-state automata (FSAs), then integrates model checking with automated test case generation to produce a complete test suite covering all states and transitions—without modifying source code or perturbing the runtime environment. Contribution/Results: To our knowledge, this is the first method enabling efficient and exhaustive test generation for Actor-model systems without requiring instrumentation or execution-environment intrusion. Evaluated on industrial-grade implementations of real-world replication protocols—including Viewstamped Replication—the approach successfully uncovered subtle, previously undetected bugs, thereby significantly enhancing the reliability and formal verifiability of distributed 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.

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.

Students commonly struggle to grasp the operational semantics of nondeterministic finite automata (NFAs) and pushdown automata (PDAs), particularly regarding multi-path computation, stack-state dependencies, and distinguishing configurations with identical control states but differing stack contents. To address this, we design and implement FSM—a domain-specific language for automata theory education—that uniquely supports full visualization of nondeterministic execution paths and dynamic stack evolution in NFAs and PDAs. We introduce a state-semantic verification mechanism to help users validate transition semantics, integrating dynamic rendering, path-traversal algorithms, and interactive state tracking. Empirical evaluation demonstrates that FSM significantly improves students’ understanding of nondeterminism and stack-dependent behavior, especially in discerning stack-sensitive state equivalence.

Behavioral modeling in robotics lacks systematic empirical understanding of the practical differences and commonalities between Behavior Trees (BTs) and State Machines (SMs). Method: We conduct the first large-scale empirical comparison across 1,200+ open-source ROS projects, leveraging domain-specific language (DSL) parsing, code mining, and conceptual mapping to analyze BT and SM usage across language design, structural abstraction, reuse patterns, and engineering practice. Contribution/Results: We find a significant upward trend in BT DSL adoption; uncover deep isomorphisms between BTs and SMs in control-flow abstraction granularity and modular reuse mechanisms; and release RoboBT-SM-Bench—the first cross-DSL, fully annotated benchmark dataset of robotic behavioral models. This work establishes an empirical foundation and infrastructure support for unifying theoretical frameworks and designing reusable architectures for behavioral modeling languages.

This work addresses the challenge of directly applying numerical time-series trajectories from cyber-physical systems (CPS) to formal verification by proposing MELA, a novel method that systematically integrates information-theoretic variable selection with decision tree–based interval abstraction to achieve fully automated, unsupervised numerical-to-symbolic conversion. By coupling this transformation with passive automata learning, MELA synthesizes compact, interpretable behavioral models from raw signals that exhibit strong correlation with underlying system states. Evaluated on two CPS case studies, MELA reduces the number of states and transitions by 49.20% on average while improving model accuracy by 41.71%, thereby effectively enabling system-level requirement verification and uncovering implicit system behaviors.

Protocol model checking often suffers from state-space explosion, particularly when channel capacity or window size increases. This work proposes a compositional verification approach based on bidirectional simulation relations, constructing a hierarchy of protocol abstractions—SCP → ABP → SWP—with progressively refined semantics. By reducing the verification of complex protocols to that of the most abstract protocol, SCP, the method circumvents direct model checking of large state machines. Correctness of ABP and SWP is then derived from the invariance properties verified solely on SCP. This abstraction-based reduction significantly lowers computational complexity and enables efficient formal verification of protocols under high parameter settings.

This work addresses the limitations of mainstream procedural programming interfaces in naturally expressing declarative problems defined over state spaces and validity conditions. It proposes a predicate-based computational abstraction that models problems as a state space equipped with Boolean predicates, where solutions correspond to states satisfying the predicate, and execution is delegated to backend strategies. By introducing a unified predicate abstraction and semantics-preserving contracts, the approach decouples problem specification from solver implementation, enabling composable realizations across diverse backends—including constraint solvers, probabilistic inference engines, and quantum oracles—while preserving semantic equivalence. Notably, this framework enables seamless adaptation of declarative problems to quantum execution without requiring manual rewriting into quantum circuits, thereby establishing a general-purpose bridge between high-level problem descriptions and heterogeneous computational backends, including quantum computing platforms.

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.