technical sales qualification

Existing research lacks systematic methods to assess how requirements engineering (RE) impacts downstream development activities, hindering RE process optimization. Method: This paper proposes the first fitness-for-purpose RE impact assessment model, integrating a systematic literature review with multi-source empirical data to identify and structure 24 downstream development activities affected by requirements and 16 quantifiable attributes. Contribution/Results: The model bridges two critical gaps in requirements quality assessment—namely, the “activity dimension” and “measurability of impact”—by enabling empirical analysis of how specific requirements artifacts and processes concretely influence development practices. It provides a theoretically grounded framework and evidence-based decision support for precise, targeted optimization of the RE phase.

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.

Software engineering research has long lacked a structured methodology to guide researchers in formulating industrially relevant research questions. Method: This paper proposes and empirically validates a seven-dimensional problem modeling framework—comprising Actual Problem, Context, Impact, Practitioners, Evidence, Goal, and Research Question—and innovatively incorporates financial dimensions (e.g., ROI) and feasibility constraints to enhance industrial applicability. We conducted an empirical evaluation with 42 senior SE researchers via participatory workshops using Problem Vision boards, structured questionnaires, and qualitative analysis. Contribution/Results: The framework significantly improves the practical relevance and operationalizability of research questions. It yields actionable guidelines for refining problem formulation, thereby effectively bridging the gap between academic research and industrial needs.

Contemporary technical hiring practices suffer from stress-induced bias and evidentiary gaps, resulting in distorted competency assessments and compromised fairness. Method: This paper proposes an evidence-driven paradigm for software engineer competency evaluation. It systematically identifies and bridges evidentiary gaps in technical hiring through (1) multi-source behavioral data integration, (2) low-stress, authentic task design, and (3) a verifiable fairness framework grounded in educational measurement, human-computer interaction evaluation, algorithmic fairness auditing, and structured competency modeling. Contribution/Results: The approach yields a scalable, empirically validated hiring effectiveness metric suite. Empirical evaluation demonstrates significant improvements in employer hiring accuracy. Crucially, it establishes a reproducible, auditable foundation for equitable assessment—enhancing both validity and procedural fairness for candidates while enabling rigorous, transparent evaluation of hiring systems.

This study addresses the challenge of early-stage technology opportunity identification, where ambiguous user needs and the absence of systematic integration of end-user values often lead to misalignment between technological potential and market demands. To bridge this gap, the authors propose a novel decision-support framework that integrates Technology Readiness Levels (TRL) with Schwartz’s theory of basic human values—introducing, for the first time, human values into the process of technology opportunity recognition. The framework defines two key metrics: “value breadth” and “vision gap.” Through qualitative analysis combining expert and consumer workshops in a case study at Sony CSL, the research demonstrates that successful technologies resonate across a broader spectrum of human values, and that experts articulate richer value dimensions than consumers. These findings validate the framework’s capacity to enhance technology–market fit through a value-driven approach.

This study addresses the prevalent ambiguity, inconsistency, and incompleteness in articulating explainability requirements for AI systems due to a lack of standardized specifications. Through a structured literature review and interviews with developers, the authors identify a set of explainability quality attributes, which are then refined via a large-scale survey of practitioners into ten core attributes. For the first time, these attributes are translated into a prioritized, actionable guideline for writing explainability requirements. Building on this foundation, the authors design a lightweight, iterative requirements engineering workflow augmented by a large language model to assist in requirement generation. An accompanying web-based tool reduces average requirement drafting time by 23.5%, and user evaluations indicate that the generated requirements match or slightly exceed manually written ones in terms of implementability and textual quality.

This study addresses the persistent challenge in software engineering research of empirically validating theories due to the absence of systematic, reproducible operationalization methods. To bridge this gap, the authors propose an integrated methodological framework that combines Sjøberg’s operationalization approach with Dubin’s theory-building methodology, offering the first evidence-driven and replicable guide for operationalizing theoretical constructs in software engineering. The approach systematically translates abstract theories into measurable forms by rigorously defining variables, selecting appropriate indicators, and deriving non-causal assumptions. The utility of the framework is demonstrated through its application to a theory on DevOps team classification. The resulting methodology provides researchers with a robust foundation for conducting verifiable theoretical studies while simultaneously offering practitioners actionable, theory-informed insights.

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.

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.

This work addresses a critical gap in the evaluation of software engineering agents, which has predominantly focused on code implementation while neglecting their ability to detect and correct defects in requirements specifications—such as omissions, ambiguities, and inconsistencies. We propose the first evaluation framework centered on specification-level reasoning, constructing a benchmark based on the RFC (Request for Comments) processes of open-source projects. The framework requires agents to systematically identify design flaws by synthesizing initial proposals, code repositories, and historical discussions. Evaluations across five repositories, including Kubernetes and React, reveal that even the best-performing model (GPT-5.4) achieves only 44.4% accuracy, highlighting a significant limitation in current agents’ capacity for requirement analysis and design review without execution feedback. This study thus fills a crucial void in assessing agent capabilities at the specification stage.