Abstract
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.