Abstract: A method or system for generating recommendations based on detected correlations of static and dynamic data. For example, the system generates feature inputs for the machine learning model that is based on labeled static data as well as dynamic data indicating current usage. Notably, the dynamic data is not retrieved from a data store of known training data, but instead is streamed in real time from active databases accessible to the network. Furthermore, the system provides a formatting mechanism that translates user selected requirements and parameters from a human-readable format indicating particular attributes of a database to optimize (e.g., database performance, security, compliance, capacity planning, etc.) into data that may be included in a machine learning feature input that specifies which correlations to use (e.g., server operational statistics data, server log data, monitoring metrics, etc.).

Abstract: A system method of managing a software repository. The method including receiving a dataset comprising a set of computer instructions, retrieving feature-related data from the dataset, determining, by a machine learning model executing on the computing device, a function of the computer instructions based on the feature-related data, and generating a synopsis of the function of the computer instructions. The method can include transmitting the synopsis to a user interface and receiving feedback from a user indicative of whether the synopsis accurately describes the function of the computer instructions. If the feedback indicates that the synopsis accurately describes the function of the computer instructions, the method can include storing the synopsis in a memory of the computing device. If the feedback indicates that the synopsis inaccurately describes the function of the computer instructions, the method can include generating a revised synopsis of the function of the computer instructions.

Abstract: A method or system for generating recommendations based on detected correlations of static and dynamic data. For example, the system generates feature inputs for the machine learning model that is based on labeled static data as well as dynamic data indicating current usage. Notably, the dynamic data is not retrieved from a data store of known training data, but instead is streamed in real time from active databases accessible to the network. Furthermore, the system provides a formatting mechanism that translates user selected requirements and parameters from a human-readable format indicating particular attributes of a database to optimize (e.g., database performance, security, compliance, capacity planning, etc.) into data that may be included in a machine learning feature input that specifies which correlations to use (e.g., server operational statistics data, server log data, monitoring metrics, etc.).

Abstract: A system method of managing a software repository. The method including receiving a dataset comprising a set of computer instructions, retrieving feature-related data from the dataset, determining, by a machine learning model executing on the computing device, a function of the computer instructions based on the feature-related data, and generating a synopsis of the function of the computer instructions. The method can include transmitting the synopsis to a user interface and receiving feedback from a user indicative of whether the synopsis accurately describes the function of the computer instructions. If the feedback indicates that the synopsis accurately describes the function of the computer instructions, the method can include storing the synopsis in a memory of the computing device. If the feedback indicates that the synopsis inaccurately describes the function of the computer instructions, the method can include generating a revised synopsis of the function of the computer instructions.