model requirements specification

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 addresses the challenge of ensuring program correctness in natural language-to-code generation, which is often hindered by the absence of high-quality formal specifications. The authors propose VeriSpecGen, a framework that decomposes natural language requirements into atomic clauses through a traceable refinement mechanism, generates requirement-driven tests with explicit traceability mappings, and synthesizes formal specifications aligned with user intent by localizing and repairing faulty clauses upon verification failure. Integrating large language models (e.g., Claude Opus 4.5) with the Lean proof assistant, the approach leverages refinement trajectories to generate 343K training samples, substantially enhancing model generalization and reasoning capabilities. Evaluated on the VERINA SpecGen benchmark, VeriSpecGen achieves an accuracy of 86.6%, outperforming the best baseline by up to 31.8 percentage points and demonstrating a relative improvement of 62–106% in specification synthesis performance.

This work addresses the high cost of manually writing formal specifications and the limitations of existing large language model (LLM)-based approaches that require white-box access to source code, thereby posing intellectual property and deployment constraints. The authors propose a black-box-driven method that leverages only test code and dynamic execution traces to generate candidate Java Modeling Language (JML) specifications via an LLM. These candidates are locally validated using bounded model checking, and an iterative feedback loop refines them based on verification outcomes. This approach is the first to enable fully automated formal specification generation without any access to the program’s internal structure. Evaluated on the SpecGenBench benchmark, it demonstrates that test-derived information effectively guides specification synthesis, while also highlighting critical challenges in checker compatibility and diagnostic feedback, substantially enhancing industrial applicability.

本文提出了一种形式化的语义块模型和执行评判基准来独立评估规范质量，通过结构化表示和机器可验证条件解决规范确定性问题。

Existing conformance checking approaches between process models and reference models suffer from limited semantic expressiveness and insufficient automation, hindering fine-grained compliance verification. This paper proposes a semantic consistency checking method grounded in causal dependency analysis of tasks and events, transcending traditional trajectory-based dependency modeling by formally encoding causal constraints at the semantic level. We establish a unified framework integrating causal dependency modeling, semantic representation, and formal verification, and design an automated conformance checking algorithm implemented in a prototype tool. Empirical evaluation demonstrates that our approach significantly outperforms state-of-the-art techniques in both accuracy and flexibility, achieving— for the first time—the fully automated, high-expressivity semantic conformance verification of process models against reference models.

This work addresses the challenge of reliably conveying intent, requirements, and constraints in human–AI–tool collaborative software development by proposing a specification-centric Bosque API (BAPI) ecosystem. The system introduces a highly expressive specification language that, for the first time, enables cross-language interoperability, automated test generation, formal verification, and execution sandboxing across the entire API lifecycle—from requirement definition and implementation to invocation and validation. By providing end-to-end specification guarantees, BAPI significantly enhances system correctness, security, and the efficiency of human–AI collaboration, offering a novel infrastructure for software development in the era of AI agents.

This work addresses the problem of implementation drift in evolving distributed systems, where runtime behavior gradually deviates from the original design. To tackle this issue, the paper proposes a design conformance assessment method based on distributed tracing data. It introduces, for the first time in the domain of distributed systems, conformance checking techniques from process mining, leveraging runtime traces collected via the OpenTelemetry standard and automatically comparing them against behavioral models defined at design time to quantify their alignment. The key contribution lies in establishing persistent, monitorable conformance metrics that enable continuous, automated evaluation of deviations between system implementation and design. This approach is readily applicable to modern distributed systems widely adopting OpenTelemetry for observability.

This work addresses the lack of end-to-end traceability from high-level models to generated code in Model-Driven Engineering (MDE) by proposing the ProMoTA framework. ProMoTA unifies the entire modeling and code generation process—spanning platform-independent models, platform-specific models, and final code—through megamodels and model transformation chains. The framework innovatively extends the Acceleo language to support fine-grained local traceability and, for the first time, enables comprehensive global traceability mapping and analysis across the full MDE lifecycle. Implemented on the Eclipse platform, ProMoTA’s effectiveness in facilitating end-to-end traceability analysis is empirically validated through a case study in wireless sensor network-based Internet of Things applications.

This study addresses the lack of traceable, structured linkage between high-level requirements and low-level automated testing in AI-enabled cyber-physical systems, which hinders compliance with regulatory demands for verifiable evidence. To bridge this gap, the paper introduces VNVSpec, a novel framework that enables end-to-end automated traceability and closed-loop verification from high-level engineering requirements to test cases. VNVSpec employs machine-readable verification and validation (V&V) specifications to support requirement ingestion, quality checks, metric-driven decomposition, test result association, and generation of audit-ready reports, all integrated into a continuous integration pipeline. Empirical evaluation demonstrates that the approach verifies 36 requirements against 449 tests in linear time, scales to tens of thousands of artifacts, and is fully reproducible through open-sourced code, test suites, and benchmark scripts.

This study addresses the challenge of transforming stakeholder requirements into product requirements in software-driven automotive systems. Leveraging a dataset of 8,082 stakeholder requirements and 5,870 product requirements provided by Infineon, the research employs a hybrid methodology integrating structural statistics, decision modeling, traceability mining, textual analysis, and hardware-software linkage to systematically analyze the requirement refinement process. It reveals, for the first time, that requirement complexity primarily stems from ambiguous architectural scope and missing contextual information rather than linguistic redundancy. The work establishes a classification framework for mapping stakeholder to product requirements, identifies systematic differences across abstraction levels, and proposes key improvements in requirement validation, deviation management, and contextual tooling to support efficient and reusable automotive development.