Systematic Literature Review on Machine Learning and its Impact on APIs Deployment Jose Rojas Valdivia, Javier Gamboa-Cruzado, Percy de la Cruz Vélez de Villa Universidad Nacional Mayor de San Marcos, Facultad de Ingeniería de Sistemas e Informática, Peru {josegerman.rojas, pdelacruzv}@unmsm.edu.pe, jgamboa65@hotmail.com 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. Keywords. Artificial intelligence, machine learning, deployment, APIs, finance, systematic literature review. 1 Introduction The detection of errors in the deployment processes either at the level of API's development, software using machine learning significantly reduces the implementation costs of these new requirements because their deployment dates would be prolonged by not being able to detect these errors in time. Machine learning is evolving over the years in the IT area, companies are successfully implementing its use in business processes that involve return on investment in the short term, it is currently used to detect errors in systems before they happen, its method of action is proactive and not reactive because it is based on concrete facts and depending on it makes an internal analysis and determines the best way forward, this guided not only by Artificial Intelligence but also by a analyst who validates the proper functioning of the implemented. To use machine learning it is first necessary to define whether it will be supervised or unsupervised [10, 5], once the type of machine learning to be used has been identified, it can be validated if a framework can be used or the development can be created from scratch, also the best known machine learning algorithms can be used such as linear regression [79], logistic regression [46, 57], decision tree [22, 23] among others, in order to meet the needs of what is presented. The main objective of this study is to identify the state of the art of machine learning and its impact on API/software deployment processes and early error detection. The paper is organized as follows. Section II presents the Background and related works, Section III details the Review Method, Section IV presents the Results and Discussion. Computación y Sistemas, Vol. 27, No. 4, 2023, pp. 1107–1124 doi: 10.13053/CyS-27-4-4371 ISSN 2007-9737