This study explores the correlation between the popularity of open-source repositories and their quality, as assessed using static code quality metrics. The primary focus is on defining key indicators for two distinct paradigms, namely functional and object-oriented programming, and developing a code search method to systematically process repositories retrieved during the search process.
This article examines the theoretical foundations of the dual control algorithm in the context of machine learning, focusing on its application for intelligent agents in multi-agent information-measuring systems. A proposed algorithm combines anomaly detection in data with telemetry-based sensor calibration, opening new possibilities for improving the accuracy and reliability of data in complex and dynamic environments. The advantages of the algorithm are analyzed concerning adaptability, forecasting, and data integration, comparing it with other machine learning algorithms.