design review

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 study addresses the time-consuming and inefficient nature of manual code review by conducting a structured systematic literature review (SLR) of 119 publications—the first to propose a comprehensive, task-dimensional taxonomy for automated code review. Methodologically, it integrates machine learning, information retrieval, program analysis, and natural language processing techniques, and empirically evaluates approaches using datasets from GitHub, Gerrit, and other platforms, with metrics including BLEU, F1, and MAP. Key contributions are: (1) a refined classification of 12 automated review tasks and 7 core technical paradigms; (2) a curated inventory of 32 publicly available tools and datasets; (3) identification of critical bottlenecks in data-driven methods—particularly regarding interpretability and cross-project generalizability; and (4) a reproducible evaluation benchmark alongside four concrete directions for future research. The findings are synthesized into a rigorous, structured SLR report.

Systematic Literature Reviews (SLRs) in software engineering frequently suffer from validity threats due to omitted or inadequately executed steps, and lack an actionable, quality-improvement framework. Method: This paper introduces, for the first time, the Capability Maturity Model Integration (CMMI) maturity paradigm into SLR process modeling, proposing MM4SLR—a five-level, incremental maturity model grounded in 39 key practices, 9 goals, and 5 process areas. The model was designed via literature-driven identification, clustering analysis, and level mapping, and empirically validated across four published SLRs. Contribution/Results: MM4SLR enables effective diagnosis of SLR quality deficiencies, supports researchers in selecting context-appropriate practices, and facilitates continuous process improvement. It constitutes the first structured, assessable, and evolutionary maturity framework for enhancing the rigor, standardization, and credibility of SLRs in software engineering.

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.

Conventional requirements engineering tools lack direct access to SysML architecture models, leading to redundant requirement definitions, semantic fragmentation, and broken traceability. Method: This paper proposes an executable, structured requirements metamodel that integrates INCOSE requirements writing practices with SysML modeling capabilities. Strictly aligned with ISO/IEC/IEEE 29148 and INCOSE guidelines, it leverages a SysML Profile extension, an MBSE integration framework, and a compliance rule engine to enable native interoperability between requirements and architecture models. Contribution/Results: The metamodel was deployed and validated on two real-world NASA JPL space systems. It significantly improves requirement semantic completeness and verifiability, enhances coverage of the NASA Systems Engineering Handbook checklist, and—critically—provides the first empirical evidence of rapid improvement in requirements expression quality. The evaluation also identifies key bottlenecks in current toolchains regarding automated support for such integrated practices.

This work addresses the longstanding reliance on manual, time-consuming, and subjectivity-prone approaches in software architecture quality assessment, particularly in scenarios involving trade-offs among multiple quality attributes. To overcome these limitations, the study introduces generative large language models (LLMs) into this domain for the first time, leveraging Microsoft Copilot in conjunction with the Architecture Tradeoff Analysis Method (ATAM) and quality attribute scenario techniques. The proposed approach enables automated identification of architectural risks, analysis of sensitivity points, and generation of trade-off recommendations. Experimental results demonstrate that, in most cases, the method achieves higher accuracy and efficiency compared to human-led reviews, substantially reducing evaluation costs while improving consistency across assessments.

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 inefficiencies in requirements management within large-scale agile development, stemming from the absence of a unified requirements engineering process and high-level guiding principles. Through a five-year longitudinal industrial case study encompassing over 25 sprints, more than 320 weekly meetings, seven cross-organizational workshops, and focused group interviews, the research employs thematic analysis to distill six transferable and scalable core principles—such as architectural context, stakeholder-driven validation, and lightweight documentation evolution. Validated across multiple multinational enterprises, these principles significantly enhance requirements management effectiveness in large-scale agile settings. This work presents the first systematic strategic requirements engineering framework tailored specifically for such complex environments.

This work addresses the lack of dedicated datasets and systematic evaluation methodologies for automatically generating software architectures from requirements documents. It introduces R2ABench, a benchmark that comprises a novel dataset of real-world requirements documents paired with expert-annotated PlantUML architecture diagrams, along with a hybrid evaluation framework integrating structural diagram metrics, multidimensional human assessments, and architectural anti-pattern detection. Experimental results demonstrate that while large language models can generate syntactically valid architectures containing key entities, they exhibit significant limitations in relational reasoning. Code-specialized models show modest improvements, whereas agent-based workflows do not consistently enhance performance. This study establishes a standardized benchmark and a multifaceted evaluation suite for the requirements-to-architecture generation task.