A Systematic Review on Continuous Improvement with DevOps

Abstract

Robustness and lifespan of a software play a major role in inclination of its trust, popularity and usage. Reducing failures, cost factor and accidental situations is the major factor for every organization and the business relies on hooking and implementing effective testing methodologies from plenty of resources and methodologies. In the previous works authors have discussed the importance of destructive testing for software systems which will impact on the efficiency of the software. Although there isn’t persistent effort on implementing such testing strategies despite its importance in the name of data loss, money and space. While delivering a model, it is effective to have continuous testing in the flow along with continuous integration and continuous deployment (CI/CD) in the concept of DevOps. Modelling the AI complex models through software engineering emerges a field of ModelOps that integrates machine-learning techniques by sandwiching the model with the application, which could be used for prediction or optimization. To ensure the use of critical testing methods, this paper reviews the various study put forward by different authors on DevOps and testing. Thereby it could be adopted for critical applications so that to reduce the data loss and increase the efficacy of the model developed by the data scientists to uplift the efforts which could be applied to all critical applications and platforms.