In the age of continuous delivery and microservices, the difference between resilient software and production chaos lies in proactive fault detection. This book shows how to harness the power of predictive analytics and DevSecOps to find weaknesses before they cause outages or security issues.
Blending software engineering theory with actionable implementation guidance for distributed, containerized environments, the book will teach you how to identify high¿risk services before deployment, improving reliability and security. This book revisits the classical approach of using object¿oriented metrics and linear regression but significantly enhances it through factor analysis. Beyond the math and metrics, this book offers a practical roadmap for building a fault¿aware DevSecOps culture. It helps you connect predictive insights to real¿time decisions, improving reliability, security, and deployment confidence across distributed systems. You will also learn about architectural guidelines on embedding fault¿prediction engines into Kubernetes¿based orchestration platforms for runtime monitoring.
Bridging a critical gap between predictive analytics in software quality assurance and modern DevSecOps practices, it establishes a viable pathway for using statistical modeling techniques not just to predict defects, but to inform actionable security and operational decisions in real¿time distributed systems.
What You Will Learn



- How to apply statistical fault prediction using factor analysis in modern SDLC workflows


- How to integrate fault detection engines into CI/CD pipelines using DevSecOps practices


- Reduce production failures in microservices-based systems


- Apply OO metrics like CK and Halstead in predictive models