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 highrisk services before deployment, improving reliability and security. This book revisits the classical approach of using objectoriented 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 faultaware DevSecOps culture. It helps you connect predictive insights to realtime decisions, improving reliability, security, and deployment confidence across distributed systems. You will also learn about architectural guidelines on embedding faultprediction engines into Kubernetesbased 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 realtime 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
Who This Book Is For
This book is for software engineers, DevOps and DevSecOps professionals, SREs, and researchers interested in building reliable, fault-tolerant systems.
Chapter 1- Introduction to Software Fault Prediction in DevSecOps.-
Chapter 2 - Statistical Foundations for Fault Detection.
Chapter 3 -
Integrating Fault Prediction into CI/CD Pipelines.
Chapter 4 - Case Study:
E-Commerce Microservices Architecture.
Chapter 5 - Security Implications and
DevSecOps Alignment.
Chapter 6 - Advanced Topics in Fault Prediction.-
Chapter 7 - Building a Fault Prediction Framework.
Chapter 8 -
Organizational Adoption and Change Management.
Chapter 9 - Conclusion and
Future Outlook.
Deepak Sharma is Associate Director at the School of Open Learning, University of Delhi, and a seasoned researcher in software engineering and fault prediction, with over a decade years of experience . He holds a Ph.D. in Computer Science and multiple advanced degrees in technology and applications. His academic work focuses on software quality improvement, defect prediction models, and statistical analysis of software metrics. He has published extensively in international journals and conferences and has been recognized by several government bodies for his research contributions.
Aamiruddin Syed is a cybersecurity engineer and DevSecOps practitioner with deep expertise in secure software development, cloud-native architectures, and supply chain security . He has led DevSecOps implementations across global enterprises, integrating predictive risk models into CI/CD pipelines and Kubernetes-based environments.
Aamiruddin is the author of Supply Chain Software Security: AI, IoT, and Application Security (Apress, 2024) and a frequent speaker at DEFCON, Black Hat MEA, RSA and other international cybersecurity forums. His insights ensure that the book translates rigorous models into actionable, real-world strategies for developers, security engineers, and SRE teams.
