timing analysis and schedulability verification

Verifying concurrent scheduling—especially preemptive scheduling—in operating systems is notoriously challenging; manual review and unit testing often fail to cover complex execution paths, leading to undetected race conditions and logical flaws. This paper introduces a novel, co-designed paradigm for concurrent software development targeting the autonomous-driving OS Awkernel, integrating model checking with runtime verification. We formalize the scheduler using TLA+/PlusCal and perform exhaustive model checking; concurrently, we inject custom assertions and lightweight tracing probes into the Rust async runtime to enable semantically consistent, bidirectional closed-loop validation. This approach bridges the semantic gap between abstract modeling and concrete execution. Applied to Awkernel, it successfully uncovered multiple race conditions and scheduling logic defects invisible to conventional testing methods, thereby significantly enhancing the reliability assurance of preemptive asynchronous scheduling behavior.

In online monitoring of embedded real-time systems, observation signals are subject to parametric communication delays exhibiting variability, causing conventional timed automata–based verification to yield inaccurate results. Method: This paper proposes the first purely zone-based online monitoring algorithm, implemented directly within the UPPAAL toolchain via symbolic execution and zone calculus—bypassing computationally expensive parameterized timed automata verification. The algorithm explicitly models and handles delay uncertainty, ensuring temporal robustness of monitoring outcomes. Contribution/Results: Experimental evaluation on real hardware demonstrates that the approach achieves both high efficiency and high precision under realistic observation conditions. It significantly enhances the feasibility and practicality of runtime verification for delay-sensitive applications, establishing a foundation for robust, lightweight online monitoring in resource-constrained embedded environments.

This work addresses the challenges of model uncertainty and unpredictability in partially observable or black-box systems during runtime by proposing a unified theoretical framework that integrates epistemic logic with temporal logic. Leveraging automata theory, it systematically formalizes core concepts—including specification, diagnosis, opacity, and monitorability—and synthesizes lightweight online monitors through offline analysis. The approach is extended to real-time systems, resolving key issues related to their temporal semantics and algorithmic complexity. Furthermore, the study precisely characterizes the fundamental limits of runtime verification, thereby establishing a constructive and implementable foundation for practical deployment of monitoring mechanisms.

This work addresses the challenge of online monitoring for real-time systems, where temporal properties are specified in Metric Interval Temporal Logic (MITL) and recognized by Timed Büchi Automata (TBA). We propose an efficient symbolic online monitoring method grounded in zone-based representation. To handle timing uncertainty, we introduce, for the first time, a time-divergence simplification mechanism; additionally, we design a minimum-time estimation strategy enabling early conclusive verdicts. Compared to conventional approaches, our method significantly improves monitoring efficiency and robustness—achieving low-overhead, high-accuracy online decision-making and predictive judgment across diverse real-time scenarios. The framework advances formal monitoring for uncertain real-time environments by unifying symbolic reasoning with proactive timing analysis, establishing a novel paradigm for runtime verification under timing imprecision.

Runtime verification of temporal properties—such as those expressed in Metric Interval Temporal Logic (MITL)—is challenging in partially observable real-time systems, particularly when critical internal events (e.g., latent faults) remain unobservable. Method: This paper proposes an active prediction approach grounded in prior formal assumptions: system behavior is modeled as a timed automaton and integrated into a runtime verification framework to enable online inference of unobservable internal events. For the first time, formal system assumptions are deeply embedded into the real-time monitoring pipeline, combining constraint-driven temporal observation modeling with assumption-guided online verification. The approach is implemented within the UPPAAL toolchain. Contribution/Results: Experimental evaluation demonstrates that the method predicts property satisfaction/violation up to several time units in advance. In case studies involving smart grids and medical devices, monitoring success rates for properties dependent on unobservable events improve by 47%, significantly enhancing both the foresight and completeness of runtime verification.

This work addresses the limitation of existing formal methods, which are predominantly used for post-hoc verification, and the difficulty of modeling timing constraints in Event-B despite its support for correctness-by-construction. To bridge this gap, the paper proposes a non-intrusive, tool-supported mechanism for embedding time semantics into Event-B. By introducing clock variables and adopting timed automaton semantics, the approach seamlessly extends Event-B’s refinement framework to accommodate real-time constraints, while leveraging Event-B’s expressive first-order logic and set theory for precise modeling. Case studies demonstrate that the method effectively enables the systematic derivation of Timed Event-B models from timed automata, significantly enhancing the stepwise development and verification of complex real-time systems.

This work addresses the challenge of verifying correctness across all valid instances of configurable Software-Defined Radio Access (SRA) systems composed of asynchronous processes coordinated by domain-specific sched日晚间. Traditional approaches struggle to verify such systems holistically due to their parameterized and infinite nature. To overcome this, the paper proposes a contract-based deductive verification framework that integrates compositional proof rules, automated method summarization for scheduler invocations, and simplification of configuration-space constraints. The approach handles quantified reasoning within an object-oriented first-order logic setting and leverages Dafny as its verification backend. This is the first method capable of delivering a unified correctness proof for an infinite family of SRA instances, thereby breaking the scalability barrier in verifying parameterized asynchronous systems. Experimental results on industrial case studies demonstrate the framework’s effectiveness in enabling efficient, automated reasoning about complex parameterized behaviors.

Traditional schedulability analysis for real-time systems relies on manual proofs, which are difficult to scale, while mechanized verification approaches based on frameworks like PROSA or ROCQ, though rigorous, demand substantial domain expertise and extensive manual scripting. To address this challenge, this work proposes PROVE-RT, the first framework that integrates retrieval-augmented generation with a staged synthesis strategy to automatically produce mechanized proof scripts using large language models. PROVE-RT introduces dependency-aware informal sketches, a PROSA-specific corpus, and a staged skeleton-guided mechanism to significantly enhance generation quality. Experimental results demonstrate that PROVE-RT achieves a success rate of 44.7% on the evaluation benchmark, substantially outperforming mainstream large language models under direct prompting, thereby advancing the automation of schedulability analysis in real-time systems.

This study addresses the limitations of existing SysML verification approaches, which are often tool-dependent and restricted to performance properties, lacking support for automated validation of behavioral and interface requirements. To overcome these shortcomings, this work proposes a tool-agnostic, automated verification workflow driven by SysML test cases, integrating UML Testing Profile and behavioral diagram constructs to enable unified validation of multidimensional attributes—including behavior, timing, and state responses. The methodology was developed through a mixed-methods research strategy combining literature review and stakeholder interviews, and its efficacy was empirically validated across two independent SysML toolchains. The approach not only transcends the constraints of conventional parametric methods but also enables automatic traceability of verification results back to the original model elements.

This work proposes CopilotVerifier, an automated verification framework designed to enhance the correctness and trustworthiness of runtime monitoring code in safety-critical systems by complementing the Copilot compiler. CopilotVerifier is the first to decompose the bisimulation relation between source programs and their compiled C code into verifiable conditions. By integrating symbolic execution (via Crucible) with SMT solving (through What4), the framework automatically generates formal proofs that guarantee semantic equivalence—ensuring identical outputs and consistent crash behaviors under equivalent inputs. This approach significantly strengthens compiler assurance with modest computational overhead and lays the groundwork for producing human-auditable formal arguments of correctness.