opportunity qualification

To reconcile the stringent DO-178C Level A safety certification requirements with the escalating complexity of avionics software, this paper proposes an airworthiness-compliant, customized Scrum framework. The method introduces a multidisciplinary Product Owner role, dual acceptance criteria—functional and certification-oriented—separate independent test/documentation teams, and a dedicated Certification Coordinator. It integrates continuous integration/delivery, automated documentation generation, and rigorous configuration management. These innovations enable deep coupling between agile iteration and regulatory compliance. Empirical evaluation demonstrates significant improvements over the traditional waterfall model: a 76% reduction in average requirement effort per engineer, 75% faster defect detection, 78% higher defect resolution efficiency, and over 50% lower defect density—all while fully satisfying DO-178C Level A certification objectives.

Existing process conformance checking techniques identify deviations between process executions and models but cannot assess their desirability—i.e., whether they are problematic, acceptable, or beneficial—leading to subjective, inefficient, and non-reproducible manual evaluation. To address this gap, we propose the first structured, reproducible framework for assessing deviation desirability. Grounded in a systematic literature review and semi-structured expert interviews, the framework defines three mutually exclusive desirability categories—problematic, acceptable, and beneficial—each accompanied by actionable recommendations that integrate theoretical conceptualization with frontline practical insights. We empirically validate the framework through task-oriented experiments, demonstrating significant improvements in analysts’ assessment efficiency and inter-rater consistency. Crucially, it maintains comprehensiveness while supporting concise, actionable decision-making. This work provides a methodological foundation for evidence-based process deviation governance.

This work proposes a systematic approach to derive task effectiveness requirements in the absence of explicit user needs. The method deconstructs task intent into context, functionality, constraints, critical dimensions, performance attributes, and architectural solutions, and introduces a task complexity factor to quantify the impact of external challenges and technology maturity. By integrating Best-Worst Scaling, it prioritizes critical dimensions based on stakeholder judgments. Through task decomposition modeling and quantitative complexity analysis, the framework supports integration with UAF/SysML artifacts and establishes a traceable mechanism for generating Tier 1 and Tier 2 requirements. The approach is validated using a close air support mission case study, effectively addressing a critical gap in requirements engineering when clear initial inputs are unavailable.

This study addresses the ambiguity in defining the Research Software Engineer (RSE) role and the absence of standardized competency criteria. Employing a Delphi method combined with multi-institutional case studies—and integrating educational competency mapping with career development theory—it constructs the first cross-institutional, hierarchical, and scalable RSE competency framework. The framework innovatively proposes a four-dimensional competency model encompassing technical proficiency, collaborative practice, research engagement, and research ethics. It systematically delineates core responsibilities, foundational competencies, professional values, and career progression pathways for RSEs, supporting role evolution and professionalization. The resulting framework has been established as an internationally recognized competency benchmark, formally adopted by multiple national RSE associations for training and certification, and has driven curriculum reform in RSE-related programs across over ten universities worldwide.

This study addresses the limitations of traditional construction quality control, which relies on lagging inspections that hinder timely intervention and often lead to rework and schedule delays. The authors propose the first component-level digital twin framework tailored for the construction phase, integrating inspection records, material production and concrete placement data, early-age sensor measurements, and strength prediction models to enable dynamic representation of quality status and readiness-driven decision support. By shifting quality assessment from passive document review to real-time, data-driven proactive management, the framework facilitates structured decisions—such as releasing or halting components—well before standard strength tests are completed. This approach significantly enhances the timeliness of interventions, traceability, and overall management efficiency in construction quality control.

Existing BPMN+DMN process models lack semantic-level automated verification; mainstream tools support only syntactic validation, while behavioral errors require manual execution and debugging, and model transformations remain opaque. Method: We propose the first end-to-end automated verification framework that (i) formally translates BPMN+DMN models into semantics-preserving Java programs; (ii) synthesizes interactive test plans via symbolic execution and input-domain disambiguation; and (iii) provides structured coverage analysis at both node and edge levels. Results: Evaluated on established benchmark processes from the literature, our approach significantly improves semantic defect detection, achieves an average test coverage of 89.3%, and accelerates verification by over 20× compared to manual methods.

This study addresses the challenge faced by production system engineers in automatically verifying production line layouts due to limited knowledge of PDDL and planning theory. To bridge this gap, the authors propose a novel approach based on an Asset Administration Shell (AAS) capability model that natively generates complete PDDL planning problems directly from domain-level descriptions, eliminating the need for PDDL-specific submodels. The method integrates four Industry 4.0 standards—VDI 3682, IEC 61360-1, IDTA 02011, and IDTA 02016—to construct the AAS and employs an extraction algorithm to automatically translate multi-AAS architectures into PDDL domains. In a laboratory case study, the approach enabled engineers to systematically compare four layout variants by modifying only the AAS model, significantly lowering the barrier to adopting automated planning in industrial settings.

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 study addresses occupational burnout among Security Operations Center (SOC) practitioners, often stemming from misalignment between job demands and individual capabilities. Drawing on flow theory, the authors conduct an inductive content analysis of 106 global SOC job postings to systematically map the prevalence of certifications (e.g., CISSP), technical skills (e.g., Python, Splunk), and soft skills—particularly communication skills, mentioned in 50.9% of listings. The research reveals, for the first time, a structured pattern in the skill and certification requirements of SOC roles. These findings provide empirical grounding for achieving challenge–skill balance, refining recruitment practices, and guiding professional development. Furthermore, the study advances the discourse on flow-aligned person–job fit and sets the stage for future investigations into the impact of artificial intelligence on SOC workforce dynamics.