A Multidimensional Decision-Support Framework for Software Quality Assessment in Agile Projects

Software quality assessment in agile projects remains fragmented. Technical, process-related, and team-related indicators are often evaluated through separate models, tools, and reports. This fragmentation limits cross-project comparability and weakens evidence-based decisions for software quality improvement. To address this problem, this study proposes the Overall Software Quality Index (OSQI), a multidimensional decision-support framework for software quality assessment in agile projects. OSQI integrates code quality, process quality, and team quality into a single project-level assessment model. The framework was initially grounded in ISO/IEC 25010:2011 and is discussed in relation to the ISO/IEC 25010:2023 revision, particularly its explicit inclusion of Safety as a product quality characteristic. Since the industrial datasets used in this study were not collected from safety-critical systems, Safety was not modeled as a separate OSQI dimension in the current version; instead, it is addressed as a scope limitation and future extension. The measurement structure was defined using the Goal–Question–Metric (GQM) approach. An initial set of 49 candidate metrics was reduced to 15 core indicators. This reduction was performed using dimension-specific strategies: Random Forest-based feature importance for code quality, Delphi and Analytic Hierarchy Process (AHP) for process quality, and thematic consolidation for team quality. The selected indicators were normalized and integrated through entropy-based weighting. This process generates an interpretable composite quality score. The main contribution of OSQI is not the isolated use of these methods, but their integration into a reproducible and tool-supported framework. The framework converts heterogeneous software engineering signals into a unified decision-support index. OSQI was evaluated using industrial agile project data. The data included static code analysis outputs, issue-tracking records, team assessment results, and product outcome indicators. In an exploratory validation across five industrial projects, OSQI showed a strong positive association with Net Promoter Score (r=0.97, p=0.0076) and a strong negative association with churn rate (r=−0.97, p=0.0061). A supporting software tool was also developed to automate data integration, score calculation, visualization, and project-level comparison. The findings suggest that OSQI can support quality monitoring, project benchmarking, and evidence-based improvement decisions in agile software engineering contexts.