Assessing the Impact of AI-Augmented DevSecOps on Lead Time in Agile Release Management
Abstract
Background: : Despite increasing interest in generative artificial intelligence (AI) within DevSecOps environments, empirical evidence quantifying its impact on software delivery performance remains limited, particularly in regulated enterprise contexts. Lead time for changes is a core DevSecOps performance indicator, yet controlled evaluations of AI-augmented pipelines remain scarce. This study investigates whether on-premises generative AI integration can measurably reduce release lead time while preserving governance and quality controls. Methods A quasi-experimental within-team design was conducted across two consecutive two-week Scrum sprints in an enterprise environment developing internal sales, human resource, and biometric absence systems. Sprint 1 served as the baseline using a conventional DevSecOps pipeline. Sprint 2 introduced an AI-augmented pipeline integrating Retrieval-Augmented Generation (RAG) and Reinforcement Learning from Human Feedback (RLHF) within a GitLab–Docker CI/CD infrastructure. The primary outcome was lead time for changes. Secondary metrics included deployment frequency and change failure rate. Statistical analysis employed Welch’s t-test, effect size estimation (Cohen’s d), and confidence interval analysis. Results A total of 42 distinct changes (21 per sprint) were analyzed. Mean lead time decreased by 39.2% during the intervention sprint (Welch’s t(32.4) = 4.28, p = 0.00014), with a large effect size (Cohen’s d = 1.32) and a 95% confidence…
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