product development lifecycle

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 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.

Inconsistent definitions of “feature” across software engineering domains—particularly requirements engineering (RE) and software product lines (SPL)—impede communication, trigger rework, and reduce cross-team collaboration efficiency. Method: We conducted an empirical study across 27 mainstream open-source projects, integrating repository mining, branch behavior analysis, qualitative coding, and pattern induction to derive a data-driven, cross-disciplinary definition of feature. Contribution/Results: This work introduces the first empirically grounded, unified feature definition framework bridging RE and SPL. It identifies recurring collaboration patterns and critical bottlenecks in feature description, implementation, and management, and proposes a roadmap linking academic theory with industrial practice. The findings yield actionable guidelines for project planning, resource allocation, and inter-team coordination, advancing feature conceptual standardization and engineering practice optimization.

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.

Existing modeling frameworks lack native support for multi-path design evolution—such as branching, revisioning, and merging—relying instead on external version-control and collaboration tools. This hinders traceability and collaborative efficiency in complex systems design. Method: We propose the “Design Multiverse” paradigm, the first approach to natively embed dynamic design operations—including branching, revisioning, and merging—within the modeling environment, enabling co-evolution of model product lines and model–metamodel relationships. Grounded in the Model Federation paradigm, our approach integrates multi-model coordination with fine-grained version control to unify the management of design state snapshots. Contribution/Results: The method significantly enhances collaborative efficiency among heterogeneous stakeholders and improves end-to-end decision traceability across the system lifecycle, particularly in large-scale, multi-domain design scenarios.

This study addresses the challenges posed by the proliferation, complexity, and expanding scope of regulatory requirements in software engineering, which hinder their systematic integration into development processes. To tackle this issue, the paper proposes a viewpoint-centered, artifact-based approach to regulatory requirements engineering. The approach innovatively integrates viewpoint analysis with artifact modeling to develop the AM4RRE (Artifact Modeling for Regulatory Requirements Engineering) framework, which facilitates cross-functional collaboration and ensures consistency in compliance-driven design. Preliminary validation demonstrates that AM4RRE effectively bridges the gap between organizational regulatory processes and software development practices, enabling a shift from ad hoc compliance responses toward systematic integration. This foundational work paves the way for further empirical investigation into scalable and sustainable regulatory compliance in software engineering.

This study addresses the lack of a unified theoretical foundation in traditional requirements engineering (RE) quality assessment, which often fails to integrate artifact- and process-oriented perspectives and overlooks information transmission efficiency. To bridge this gap, the paper proposes a holistic theoretical framework that models RE as a flow of information particles among stakeholders, developers, testers, and artifacts, with information flow as its core construct. Building on this model, the authors develop a simulation system to capture dynamic interactions and information exchanges across roles. The simulation reveals how high-quality requirements specifications can be inadvertently bypassed in agile environments and yields actionable insights for improving RE processes. This work establishes a theoretical basis for optimizing information flow, enhancing RE effectiveness, and understanding the underlying causes of success or failure in requirements engineering practices.

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 investigates how goal-aligned and goal-agnostic reward mechanisms influence decision-making behavior and design diversity in creative tasks. Using a 3D parametric chair design task as the experimental setting, the design process is formalized as a Markov decision process, and a mixed-methods approach—integrating user behavior tracking, experimental psychology paradigms, and hybrid analytical techniques—is employed to systematically examine participants’ exploration strategies and subjective experiences under different reward conditions. The findings reveal that goal-aligned rewards not only enhance goal attainment but also foster more thorough exploration of the design space while preserving diversity. Moreover, the nature of the design goal significantly moderates users’ perceived usefulness of the rewards. These results elucidate the synergistic mechanism between rewards and goals and offer actionable guidelines for designing effective feedback systems in creative design contexts.

This study addresses a critical limitation of existing DORA metrics, which rely solely on first-order statistics and thus fail to capture the distributional characteristics of software release cadence or distinguish teams with markedly different release regularity. To overcome this, the work introduces second-order statistics into the DORA framework for the first time, proposing a novel Delivery Consistency (DC) metric based on the coefficient of variation of inter-release intervals. It further constructs an eight-prototype Delivery Health Matrix to enable multidimensional diagnosis and targeted intervention for software delivery rhythms across platforms. Validation using real-world data spanning 120 weeks from four platforms—including Jira, GitHub, and Firebase—demonstrates that the approach effectively identifies teams sharing identical DORA ratings yet exhibiting divergent release patterns, uncovering underlying organizational or process constraints common to such teams.