Author name: Ayoub KODDAM

1. The missing discipline in AI adoption Artificial Intelligence has become a default layer in modern engineering systems: development, testing, analytics, DevOps, and even product design. However, most organizations are currently in a paradoxical situation: They are increasing AI usage while lacking any structured understanding of its real cost structure. The result is a distorted

From Deterministic Testing to Trust Engineering in Autonomous and AI-Driven Systems Software engineering is undergoing a structural transformation. For decades, Quality Assurance was based on a stable assumption: Systems are deterministic and testable through expected outputs. That assumption is now breaking. Modern systems increasingly include: These systems do not simply execute logic. They interpret, reason,

Modern distributed systems generate an extreme volume of logs across Kubernetes clusters, microservices, CI/CD pipelines, API gateways, authentication layers and event-driven architectures. The challenge today is no longer log collection or observability. The real problem is interpretation: Traditional observability tools provide data visibility. They do not provide reasoning. This is where AI models such as

The Hidden Cost of Ignoring Testing Fundamentals in Modern Automation 1. The New Testing Paradox Software testing has entered a paradoxical phase. On one side: On the other side: This leads to a critical question: If we can automate testing so easily, why is software quality still unstable? The answer lies in a fundamental misunderstanding: