test-driven development

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.

In model-based systems engineering, low experimental data reuse efficiency and excessive redundant experiments hinder digital engineering agility. To address this, this paper proposes a case-based reasoning (CBR)-driven experimental management framework that explicitly integrates domain knowledge. The framework features structured experimental metadata modeling, digital twin–enabled scenario semantic alignment, and an interpretable similarity assessment mechanism to intelligently determine whether historical experiments can be transferred to address new verification queries. Its key innovation lies in embedding domain knowledge explicitly into both the CBR retrieval and adaptation stages, thereby enabling trustworthy cross-operating-condition and cross-configuration experimental data reuse. Evaluated on an industrial-scale vehicle energy system design case, the framework reduces redundant experiments by 37% and shortens early verification cycles by 42% on average, significantly enhancing iterative efficiency in digital engineering and advancing intelligent experimental management.

Model fidelity—the degree of correspondence between simulation and reality—lacks a formal, axiomatic foundation in digital engineering, resulting in ambiguous evaluation criteria and poor cross-domain comparability. Method: This paper introduces the first rigorous, verifiable theoretical framework for fidelity assessment, grounded in seven foundational axioms encompassing consistency, measurability, scale invariance, and other essential properties; the framework enables formal verification and comparative analysis of fidelity metrics. Empirical validation is conducted via integration into ground-vehicle modeling, demonstrating feasibility and practical guidance within existing evaluation paradigms. Contribution/Results: The work fills a critical theoretical gap in fidelity science and establishes a universal, standards-ready paradigm for fidelity assessment—directly advancing digital twin development, simulation verification and validation (V&V), and model-based systems engineering. It further provides a clear, principled roadmap for future methodological evolution and standardization.

To address the lack of systematic continuous verification and secure release mechanisms in open-source hardware design, this paper pioneers the systematic adaptation of software CI/CD paradigms to the hardware domain, proposing a general-purpose framework for automatic hardware specification mining and continuous deployment. Methodologically, it integrates HDL static analysis, machine learning–driven specification inference, formal verification, and cloud-native automated pipelines, implemented in the prototype system Myrtha. Key contributions include: (1) the first CI/CD architecture supporting continuous hardware specification generation, verification, and release; (2) a scalable, automated specification mining mechanism that overcomes traditional manual modeling bottlenecks; and (3) substantial improvements in quality assurance, experimental reproducibility, and cross-team collaboration efficiency for open-source hardware development.

In industrial quality inspection, anomaly detection suffers from poor robustness due to high noise levels and sparse defective samples. To address this, we propose Iterative Refinement of Pseudo-labels (IRP), a self-supervised method that alternately evaluates sample credibility and removes misleading instances under feature-space consistency constraints—effectively purifying the training set dynamically without human annotations and generating high-fidelity self-supervised signals. IRP introduces the novel paradigm of “iterative data refinement,” significantly enhancing model robustness against label noise and cross-domain generalization capability. Evaluated on KSDD2 and MVTec AD benchmarks, IRP consistently outperforms existing unsupervised and self-supervised methods. Notably, under high-noise conditions, it achieves substantial improvements in detection accuracy and reduces false positive rates by over 25%.

Existing CAD generation models struggle to emulate engineers’ iterative design processes and lack the capability to validate physical and structural compliance. This work proposes an industry-native CAD generation framework that produces complete multi-part STEP files from engineering text and, for the first time, integrates finite element analysis (FEA) into the generative loop to verify structural plausibility. The approach leverages structured blueprint descriptions and 21-view image renderings as dual supervisory signals to guide large language model agents—such as GPT-5.5 and Claude Code—toward self-improving generation. Evaluated on the S2O and Fusion360 datasets, the method significantly enhances geometric reconstruction quality and engineering compliance, improving Box-IoU from 0.444 to 0.592 and from 0.397 to 0.505, respectively.

This study addresses the inefficiencies and impeded knowledge transfer arising from fragmented verification and validation (V&V) practices at the Jet Propulsion Laboratory (JPL). To overcome these challenges, this work proposes a unified V&V architecture grounded in human-centered design. By decoupling methodologies while maintaining a common attribute set, the architecture achieves bidirectional traceability through relational design and platform-independent SysML modeling. Furthermore, it establishes a comprehensive toolchain by integrating the Jama platform, modular templates, and digital thread technologies. This research effectively balances engineering rigor with agility, facilitating process automation, pattern reuse, and efficient cross-project collaboration. Ultimately, it provides a scalable and unified paradigm for the V&V of complex systems.

This study addresses the complexity and error-proneness of robustness verification processes for automotive electrical and electronic components by proposing an OWL ontology-based semantic mapping method for mission profiles. By formally modeling component characteristics and mission profiles, we construct an automated verification framework that supports semantic querying to enable intelligent task selection and decision-making, effectively replacing traditional manual workflows. Applied to automotive power electronics, this approach significantly shortens design cycles while enhancing verification completeness and reliability. Ultimately, this work establishes a novel knowledge-driven paradigm for the verification of complex systems, demonstrating substantial improvements over conventional methodologies in both efficiency and accuracy.

This work addresses the lack of formal guarantees for global consistency among heterogeneous views—such as electrical, thermal, mechanical, and software—in multi-view systems engineering. It introduces sheaf theory into model-based systems engineering for the first time, constructing a topological space (an architectural site) where interfaces serve as points and engineering views as open sets. A design presheaf is defined to assign local design spaces to these opens. Using restriction maps and limit-preserving functors from category theory, the paper proves that this presheaf satisfies the sheaf condition if and only if all pairwise interfaces are compatible, thereby reducing global consistency to local compatibility and ensuring a unique global design amalgamation. The approach is machine-verified in Lean 4 with Mathlib for a three-view case study, yielding a formally checkable chain of consistency proofs.

研究探讨了在工程代理中保留或省略验证节奏指导对重新验证策略的影响，通过实验表明明确的验证节奏指导能提高重新验证率、减少违规并增加成功几率。