declarative problem specification

This study addresses the high cost, error-proneness, and poor maintainability of manually writing Linear Temporal Logic (LTL) specifications—a core bottleneck in formal verification. We systematically survey and evaluate automated LTL specification mining methods. First, we propose a unified, multi-paradigm classification framework—covering constraint solving, neural networks, enumeration-based search, formal language inference, and grammar-guided learning—marking the first such comprehensive taxonomy. Second, we introduce a standardized evaluation methodology assessing scalability, interpretability, and noise robustness across approaches. Our analysis clarifies the applicability boundaries and inherent limitations of each paradigm and yields a practical, industry-oriented selection guide. The work significantly advances the automation level and reliability of LTL specification acquisition, bridging the gap between theoretical mining techniques and real-world verification practice.

This paper addresses hyperproperties—higher-order system requirements encompassing information-flow security, knowledge reasoning, and robustness, which span multiple execution traces—by proposing the first unified logical and algorithmic framework covering the entire verification lifecycle. Methodologically, it rigorously characterizes the expressive power and decidability boundaries of classical temporal logics (LTL, CTL, S1S) over hyperproperties; then introduces a novel multi-trace synchronization modeling and quantifier alternation handling mechanism grounded in higher-order temporal logic, constraint solving, and symbolic automata. Key contributions include: (i) a comprehensive taxonomy and complexity-theoretic characterization of hyperproperty logics; (ii) an open-source verification toolchain supporting HyperLTL and HyperCTL*; and (iii) end-to-end support for core verification tasks—including satisfiability checking, model checking, runtime monitoring, and controller synthesis.

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.

Existing formal methods lack high-level verification support for hyperproperties during early system design stages; although Alloy excels at relational modeling, it natively lacks hyperproperty verification capabilities. Method: We propose HyperPardinus—the first framework integrating hyperproperty verification into Alloy—by extending the Pardinus temporal logic backend to unify relational modeling, quantified template encoding, and SAT/SMT solving, while conservatively incorporating底层 detectors (e.g., MCHyper, HyperQube). Contribution/Results: Its core innovation is a novel model-finding algorithm enabling verification of complex hyperproperties with alternating quantifiers, coupled with abstraction-level counterexample visualization. Experiments demonstrate that HyperPardinus efficiently generates human-understandable high-level (counter)examples in canonical security and concurrency scenarios, significantly enhancing the feasibility and practicality of hyperproperty verification at the design stage.

This study addresses the challenge in axiomatic design of accurately translating customer needs and constraints into a minimal and independent set of primary functional requirements (FRs). Focusing on the problem definition phase, it systematically elucidates the nature, invariance, and formulation principles of primary FRs. Building upon Nam P. Suh’s theoretical framework and integrating insights from complexity theory and requirements engineering, the work establishes—for the first time—the objectivity and uniqueness of primary FRs, clarifies common misconceptions, and critically examines the applicability boundaries of large language models in this context. The research provides designers with a clear, actionable methodology for constructing primary FRs, thereby significantly enhancing the rigor of problem definition and the likelihood of successful design outcomes.

This work presents the first systematic investigation into the capability of large language models (LLMs) to generate program specifications involving higher-order logical constructs, which are essential for expressing complex verification properties yet remain beyond the reach of existing LLMs that predominantly handle basic syntactic forms. The authors design four syntactic configurations spanning different levels of abstraction and establish a comprehensive evaluation framework to assess a range of representative LLMs on standard verification benchmarks. Experimental results demonstrate that LLMs can effectively produce valid higher-order logical expressions; moreover, integrating logical constructs with base syntax significantly enhances verification efficacy and robustness without substantially increasing verification overhead. The study also reveals distinct advantages of two refinement paradigms in specification generation.

This work addresses the challenge of effectively integrating axiomatic program logics into refinement-based proofs to verify that implementations satisfy safety specifications. We present the first systematic unification of Hoare logic, Incorrectness Logic, Lisbon Logic, and Necessary Precondition Logic, establishing their correspondence with forward and backward simulation relations. Crucially, we reduce the verification of these simulation relations to the validity of standard program logic triples, thereby enabling refinement proofs to directly leverage existing verification tools. As a practical demonstration, we successfully propagate the safety bound of an atomic sequential counter through an intermediate concurrent model to its Left-Right concurrent implementation, confirming both the practicality and effectiveness of our framework.

This work proposes a novel integration of large language models (LLMs) into the Asmeta framework to address the persistent challenge in model-based development wherein users struggle to accurately translate informal requirements into formal temporal logic specifications. The approach establishes a closed-loop, interactive workflow that supports users throughout the definition, formalization, interpretation, and verification of temporal properties, leveraging feedback from model checkers to enable effective human–machine collaboration. By incorporating LLMs as intelligent assistants within this formal methods pipeline, the methodology significantly enhances both the efficiency and accuracy of formal modeling. Empirical validation through representative case studies demonstrates the practical efficacy and promising potential of LLMs in supporting and advancing formal verification tasks.

This study addresses the limited semantic transparency and poor comprehensibility of existing conceptual models, which stem from their reliance on low-level syntactic constructs to represent domain abstractions, thereby hindering effective system design and stakeholder communication. To overcome this, the paper proposes a language-agnostic abstract symbol engineering approach that identifies, formalizes, visualizes, and validates recurring syntactic configuration patterns, replacing them with high-level, semantically transparent abstract symbols. The method is instantiated as the DeCleaR extension to Dynamic Condition Response (DCR) graphs. Empirical evaluation demonstrates that DeCleaR significantly enhances perceived model quality, pragmatic quality, and user preference compared to standard DCR graphs.

This work addresses the challenge that counterexamples generated by formal verification often consist of numerous low-level Boolean variables, rendering them difficult for developers to interpret at the application-domain level. To bridge this gap, the paper proposes a novel hierarchical explanation method that integrates predicate relevance metrics with dependency graph analysis—a first-time fusion of these two techniques—to automatically extract human-readable, domain-oriented explanations from logical formulas. By leveraging formal modeling and a dedicated explanation-generation algorithm, the approach produces concise and semantically clear descriptions of failure causes across multiple case studies. Empirical results demonstrate that the method significantly outperforms existing techniques, offering effective support for fault localization in practical verification tasks.