🤖 AI Summary
This study addresses the persistent challenge organizations face in aligning DevOps automation initiatives with strategic objectives such as waste reduction, delivery predictability, cross-team collaboration, and customer-perceived quality. To bridge this gap, the authors propose a unified VSM–GQM–DevOps framework that integrates Value Stream Mapping (VSM), the Goal-Question-Metric (GQM) approach, and DevOps practices. The framework enables identification of delivery bottlenecks, construction of decision-oriented measurement models, and implementation of maturity-aligned, reversible automation interventions, thereby establishing an auditable and traceable pathway for automation investment. Validated through a multi-site longitudinal study employing DORA metrics, interrupted time series analysis, and mixed-methods evaluation, the framework demonstrates significant improvements in delivery performance and project management outcomes, fostering continuous, strategy-aligned improvement.
📝 Abstract
DevOps automation can accelerate software delivery, yet many organizations still struggle to justify and prioritize automation work in terms of strategic project-management outcomes such as waste reduction, delivery predictability, cross-team coordination, and customer-facing quality. This paper presents \textit{VSM--GQM--DevOps}, a unified, traceable framework that integrates (i) Value Stream Mapping (VSM) to visualize the end-to-end delivery system and quantify delays, rework, and handoffs, (ii) the Goal--Question--Metric (GQM) paradigm to translate stakeholder objectives into a minimal, decision-relevant measurement model (combining DORA with project and team outcomes), and (iii) maturity-aligned DevOps automation to remediate empirically observed bottlenecks through small, reversible interventions. The framework operationalizes traceability from observed waste to goal-aligned questions, metrics, and automation candidates, and provides a defensible prioritization approach that balances expected impact, confidence, and cost. We also define a multi-site, longitudinal mixed-method validation protocol that combines telemetry-based quasi-experimental analysis (interrupted time series and, where feasible, controlled rollouts) with qualitative triangulation from interviews and retrospectives. The expected contribution is a validated pathway and a set of practical instruments that enables organizations to select automation investments that demonstrably improve both delivery performance and project-management outcomes.