José del Sagrado

Multi-objective Next Release Problem experiments require the coordinated management of instance data, requirement interactions, optimisation configurations, solver execution, and result analysis. These activities are often distributed across independent scripts, libraries, and visualisation tools, making it difficult to preserve consistent problem semantics and traceability throughout the experimental workflow. This paper presents NRP-Exp, a modular open Python framework that connects instance provision, evaluator aggregation, interaction-aware preprocessing, multi-objective problem formulation, solver execution, Pareto-front assessment, and post-optimisation analysis through common representations and reusable headless components. Its ordered preprocessing pipeline transforms descriptive NRP instances into traceable optimisation-ready variants by resolving coupling components, remapping implication relationships, processing value-based interactions, and constructing alternative branches for mutual exclusions. The framework integrates six heterogeneous optimisation algorithms through a common solver contract while preserving their applicability conditions. Validation combines an end-to-end reference workflow, six NRP formulation profiles containing between 12 and 100 requirements, and a controlled linear case for solver portability. All formulation cases were loaded and transformed successfully, producing nine optimisation-ready variants; each selected case was executed through a compatible solver, and all six solver implementations returned feasible results for the common controlled formulation through the shared solution and Pareto-front representations. These results support NRP-Exp as an integrated and traceable infrastructure for structurally repeatable and configurable NRP experiments, while highlighting that strict experimental reproducibility with stochastic solvers requires explicit random-seed management.

The incorporation of human values into software engineering processes is increasingly recognised as crucial, yet there is still a lack of practical methods to ensure their inclusion. A key challenge in this area is to identify a representative set of stakeholders while maintaining equality and diversity throughout the development process. This study aims to leverage stakeholder salience—defined by the attributes of power, legitimacy, and urgency—to partition stakeholders using a quantile-based statistical approach, establishing stakeholder groups that prevent discrimination and marginalization in requirements engineering processes. We propose a quantile-based statistical method for systematic stakeholder partitioning to ensure that diverse interests are considered and formalize equality and diversity through quantitative metrics in order to operationalize fairness. We applied the proposed method to the RALIC dataset to evaluate its applicability, considering two and three levels of granularity (binary medians and ternary terciles) for each salience component to assess its effectiveness in maintaining equality and diversity. Integrating quantile-based stakeholder salience partitioning into the identification process provides a structured approach to incorporating human values into software engineering, demonstrating that tercile partitioning significantly optimizes group balance and spatial coverage, ultimately contributing to more inclusive and equitable decision-making in requirements gathering.

Context: Large Language Models (LLMs) have catalyzed a paradigm shift in Software Engineering (SE); however, their empirical evaluation remains predominantly confined to developer-centric, static code generation tasks. The socio-technical potential of LLMs to augment Agile project management—specifically the Product Owner (PO) role, which is tasked with mediating the conflicting demands of business stakeholders and technical development teams—remains critically underexplored.Objective: This study addresses this gap by evaluating the capacity of advanced models to act as strategic, consultative co-pilots for Agile Product Owners.Methods: We introduced a structured, five-phase LLM Chat Analysis Methodology, complemented with a novel Dynamic Prototype Evaluation Protocol. We empirically assessed four leading LLMs (ChatGPT, Copilot, DeepSeek, and Gemini) using a standardized conversational protocol across four highly diverse Software Engineering (SE) case studies. Results:The evaluation revealed distinct operational personas: ChatGPT and Copilot excel at generating end-to-end ``Walking Skeleton'' prototypes that facilitate rapid stakeholder validation, whereas DeepSeek demonstrates superior architectural rigor in enforcing non-functional constraints to shield the development team. Crucially, dynamic runtime executions exposed a pervasive risk of ``silent'' technical debt—such as severe Application Programming Interface (API) misconfigurations and the models' collective failure to apply exact-precision data types for financial transactions —highlighting a profound gap between syntactical validity and runtime resilience.Conclusions: While LLMs cannot operate autonomously as system architects, they fundamentally empower the Product Owner to accelerate agile value delivery and mitigate early integration risks. To safely leverage these tools, the PO must evolve into a new operational mandate as an active Artificial Intelligence auditor.

The Next Release Problem consists in selecting a subset of requirements to develop in the next release of a software product. The selection should be done in a way that maximizes the satisfaction of the stakeholders while the development cost is minimized and the constraints of the requirements are fulfilled. Recent works have solved the problem using exact methods based on Integer Linear Programming. In practice, there is no need to compute all the efficient solutions of the problem; a well-spread set in the objective space is more convenient for the decision maker. The exact methods used in the past to find the complete Pareto front explore the objective space in a lexicographic order or use a weighted sum of the objectives to solve a single-objective problem, finding only supported solutions. In this work, we propose five new methods that maintain a well-spread set of solutions at any time during the search, so that the decision maker can stop the algorithm when a large enough set of solutions is found. The methods are called anytime due to this feature. They find both supported and non-supported solutions, and can complete the whole Pareto front if the time provided is long enough.

Information about the paper titled "GUIDELINES FOR USE SERIOUS GAMES IN SOFTWARE ENGINEERING TEACHING" at IATED Digital Library