Adaptive and Predictive Testing Frameworks Using Chaos Engineering and Deep Learning for Enterprise QA

Abstract

As enterprise software systems grow in complexity and scale, traditional quality assurance (QA) methods fall short in predicting failures and ensuring system resilience. This research proposes an innovative QA paradigm that integrates chaos engineering with deep learning to create adaptive and predictive testing frameworks. Chaos engineering introduces controlled disruptions to identify system weaknesses under real-world stress, while deep learning models analyze test outcomes to anticipate failure points and adapt testing strategies dynamically. The proposed framework supports continuous testing across microservices, cloud-native environments, and large-scale enterprise platforms. Experimental validation on simulated and real enterprise workloads demonstrates enhanced fault detection rates, reduced mean time to recovery, and intelligent test case prioritization. The results affirm that integrating chaos experimentation with AI-based learning models leads to more robust, self-improving QA processes capable of coping with the volatility and dynamism of modern software ecosystems