Systematic Literature Review on Machine Learning and its Impact on APIs Deployment

Abstract

Machine Learning is being used worldwide in the deployment of API's (Application Programming Interface). The development of machine learning presents: techniques, algorithms, sequences, logic based on facts, and predictions of future errors in various processes of organizations such as the process of deployment of API's/functionalities/software. A systematic literature review (SLR) was conducted on machine learning for the process of API/functionality deployment/error detection. The search strategy identified 176378 papers in digital libraries such as: Scopus, ProQuest, ScienceDirect, IEEE Xplore, Taylor & Francis Online, Web of Science, Wiley Online Library and ACM Digital Library; which were filtered by exclusion and quality criteria obtaining as final result, for review and analysis, 85 papers. The results of the systematic review have focused on machine learning papers recently published in recent years regarding the deployment of API's, software, monitoring and control tools, error detection where machine learning offers alternatives to improve and be more efficient in those processes that fail regularly today. The RSL has allowed a broad view on the studies and findings presented in this study.