Journal of Advanced Robotics, Autonomous Systems and Human-Machine Interaction
Role of Quality Assurance In Devops: Bridging the Gap Between Developmentand Operations
Abstract
Idowu Olugbenga Adewumi, Wumi Ajayi and Nelson Ayibawanemi John
The way we incorporate Quality Assurance (QA) into DevOps has come a long way. It is no longer just a step that happens after development; now, it is a continuous, smart process that flows throughout the entire software delivery lifecycle. This study explores how blending AI and machine learning techniques with modern QA practices can make a real difference, drawing on data from various production environments. The results are impressive: we found that defect density dropped by as much as 64.2%, the average time to repair issues fell by 42.1%, automated test coverage increased by 39.1%, and deployment success rates went up by 16.6%. By employing model-driven strategies like predicting build failures with XGBoost, generating intelligent test cases using CodeT5, and detecting anomalies through Isolation Forest, our integrated framework is able to spot risks early, enhance test execution, and speed up the release process. When we compared these outcomes to our benchmarks before the integration, it became clear that AI- enhanced QA not only reduces production defects and rollback incidents, but it also helps eliminate the bottlenecks typical of traditional QA methods. These study revealed the game-changing potential of self-healing, predictive QA systems for handling scalable, high-frequency release cycles. Nevertheless, it is important to note some trade-offs, such as the added complexity of maintaining test suites and the impact on pipeline execution time.