Intelligent Software Automation Platforms for Sophisticated Quality Engineering Applications

Abstract

In modern software applications, especially complex and evolving ones, intelligent automation frameworks for QA processes play a crucial role. Static Test Cases and Rule-Based Automation are insufficient in terms of scalability, flexibility and defect prediction in CI/CD centric environments. In this paper, a new intelligent framework for automating the end-to-end QA process using machine learning techniques, autonomous agents and feedback optimization is introduced. The frame7work will have adaptive components which will allow the system to learn and become better at testing through telemetry, test results and previously occurred defects patterns. Also, it will provide orchestration abilities for distributed testing and dynamic high risk component identification in complex software systems. This paper introduces three major contributions: First, an adaptive test case generation technique based on intelligent monitoring of system's behavior, Second, defect prediction module based on reinforcement learning to dynamically update the risk profile, Third, a decentralized multi-agent collaboration framework for effective test automation. The experiments show clear improvement in percentage of detected defects; reduced cost of maintenance and shorter release cycle compared to traditional automation frameworks.