Deep Lift Learning-Based Validation Model for Enhancing Resilience and Adoptability of DevOps Phases

Software Development (Dev) and Information Technology Operations (Ops) rely on different process parameters, such as robustness, resilience, and in-line optimizations. Resilience is a key requirement when adopting various enterprise features to ensure system stability and swift recovery from failures. Based on different enterprises’ adoption conditions, the need for resilience and its optimization is to be designed. In this article, a low-complexity, adoptable, and validated resilience model is proposed to improve the efficiency of the DevOps functional phase. This proposed model uses intrinsic deep lift features of the applications to assess its resilience. In this case, optimization is performed using trust, robustness, and resilience parameters, as per the application’s demand. A fine-to-coarse tuning strategy is applied to both individual and permuted parameters to improve the adaptability and scalability of the DevOps implementation. Considering parameter permutations, selective tuning is also feasible through deep lift learning to improve resilience with reduced complexity. This model is efficient in leveraging adoptability, achieving 12.43% for the plan and 13.17% for the feedback phases, for maximum execution time.