prioritize product features

Current what-if analysis lacks a unified conceptual framework, leading to terminological inconsistency across domains, structural ambiguity, and divergent interpretations. To address this, we conduct a systematic review of 141 papers in visual analytics and human-computer interaction, proposing Praxa—the first integrative framework that unifies scenario modeling, sensitivity analysis, and counterfactual analysis under a coherent paradigm. Praxa formally defines the underlying motivations, core components (hypothesis generation, intervention modeling, outcome evaluation), and a taxonomy of analytical types. It establishes a standardized terminology and structured model, exposing critical challenges including interpretability, causal modeling fidelity, and alignment with user intent. By clarifying conceptual boundaries and operational relationships among methods, Praxa significantly enhances cross-domain conceptual consistency and application clarity. The framework provides a rigorous foundation for theoretical advancement and the design of next-generation interactive analytical tools.

Automating the generation of user story sets for new systems in software product lines—based on existing system families’ variability logic—remains challenging. Method: This paper proposes a synergistic approach integrating Triadic Concept Analysis (TCA) and Large Language Model (LLM) prompt engineering. We pioneer the application of TCA to model the three-dimensional variability structure of “system–role–feature,” which guides LLMs to generate interpretable, semantically enriched, variability-aware user stories. Compliance, completeness, and consistency are ensured via option-guided prompting and multi-round validation. Results: Evaluated on a real-world dataset of 67 websites’ user stories, our method significantly improves requirement coverage (+23.6%) and cross-system consistency, while enabling traceable design decisions.

Process mining often yields an overwhelming number of candidate process models, creating decision paralysis for managers seeking actionable insights. Method: This paper proposes a multi-criteria decision-making (MCDM) evaluation framework that jointly incorporates quantitative metrics (e.g., fitness, precision) and qualitative factors (e.g., organizational culture alignment). It systematically integrates MCDM techniques—including the Analytic Hierarchy Process (AHP)—into process model prioritization for the first time, moving beyond purely technical, performance-driven selection criteria. Contribution/Results: The framework enables structured, interpretable trade-offs between operational performance and strategic objectives. Evaluated in a logistics case study, it significantly improves contextual sensitivity and managerial alignment in model selection, facilitating robust, transparent decision-making under competing goals.

This paper addresses the Next Release Problem (NRP)—a multi-objective software requirements selection problem under resource constraints. We propose a scalable, generic optimization framework that uniformly models customer satisfaction, development cost, requirement attributes (e.g., priority, stability), inter-dependencies, and hard/soft constraints, enabling Pareto-optimal solution generation and stakeholder trade-off analysis. Our key contribution is the first formal, open-ended NRP modeling paradigm, designed to adaptively evolve with changing problem domains. Leveraging requirement dependency graphs, multi-objective optimization, and case-driven instantiation, we replicate and extend six existing solution approaches across six industrial case studies. Empirical results demonstrate the framework’s compatibility with diverse methodologies, high customizability, and practical effectiveness in real-world settings.

Business professionals—non-technical domain experts—lack appropriate tools and methodologies for effective what-if analysis (WIA), hindering data-informed decision-making. Method: We conducted a two-phase mixed-methods user study—comprising contextual interviews and in-situ task-based evaluations—to systematically characterize their analytical behaviors for the first time. Contribution/Results: Based on empirical findings, we propose three domain-grounded design principles: business-contextual data preparation, risk-aware assessment, and domain-knowledge integration. We implemented and validated these principles in an interactive visual analytics prototype. The study identifies three critical support gaps, empirically confirms that six classes of what-if techniques significantly improve decision efficiency and confidence, and yields eight actionable design guidelines for commercial business intelligence systems. This work bridges a key theoretical and practical gap in WIA research concerning non-technical users.

This work addresses the challenges of low quality and poor transparency in build-or-buy decisions within enterprise software development, which often stem from reliance on unstructured experiential knowledge. To overcome these limitations—particularly in cold-start scenarios lacking historical data—the authors propose a structured approach that integrates a decision-factor ontology, rule-based reasoning, and reference-class matching. This method enables transparent, auditable evaluation of alternatives and represents the first application of combined ontology modeling and rule reasoning to build-or-buy decision-making. By revealing critical decision thresholds and supporting traceability, the approach enhances the rationality, transparency, and auditability of choices. Its practical efficacy is demonstrated through a lightweight tool validated in a financial industry case study, showing significant improvements in decision quality.

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 challenge users face in constructing, refining, and validating personalized evaluation criteria when making decisions based on reviews, particularly due to the frequent oversight of infrequent yet critical details. To tackle this issue, the authors propose a visual analytics system that integrates large language models with the Analytic Hierarchy Process (AHP) to automatically generate an initial decision model from user reviews and support human-AI collaborative iterative refinement of criteria, weights, and evidence provenance. The system introduces a novel coverage gap detection mechanism to identify missing evaluation dimensions, incorporates AHP-based consistency constraints for interactive weight adjustment, and enhances decision transparency through multi-level scorecards and exportable reports. Experimental results demonstrate that the system significantly improves users’ control over their evaluation criteria and the overall trustworthiness of their decisions.

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.