Application performance automation system

A non-transitory computer readable storage medium having instructions encoded thereon that, when executed by one or more processors, cause the one or more processors to perform steps comprising:

• collecting production telemetry data about an application in a production environment;
• analyzing the production telemetry data to detect performance inefficiencies of the application in the production environment,
• wherein the production telemetry data is analyzed using one or more machine learning models;
• generating, using the one or more machine learning models, a first intervention comprising a code refactor to address at least one detected performance inefficiency,
• wherein generating the first intervention comprises modifying code of the application using the one or more machine learning models based on analyzing the production telemetry data;
• deploying the first intervention onto an application in a sandbox environment for validation;
• collecting sandbox telemetry data about the application with the first intervention applied in the sandbox environment;
• analyzing the sandbox telemetry data to assess impact of the first intervention in the sandbox environment;
• determining whether performance of the application with the first intervention has improved by at least a predetermined threshold, compared to previous performance in the production environment;
• in response to determining that the performance of the application with the first intervention has improved by at least a predetermined threshold, deploying the first intervention to the application in the production environment;
• in response to determining that the performance of the application with the first intervention has not improved by the predetermined threshold,
• identifying a second intervention based on the detected performance inefficiencies of the application without the first intervention and the assessed impact of the first intervention;
• and
• deploying the second intervention onto the application in the sandbox environment for validation.

A computing system, comprising:

• one or more processors;
• and
• a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by one or more processors, cause the one or more processors to perform steps comprising:
• collecting production telemetry data about an application in a production environment, the telemetry data including one or more of a latency, CPU utilization, memory utilization, throughput, or error rate;
• analyzing the production telemetry data to detect performance inefficiencies of the application in the production environment,
• wherein the production telemetry data is analyzed using one or more machine learning models;
• identifying a first intervention based on the detected performance inefficiencies,
• wherein identifying the first intervention comprises modifying code of the application using the one or more machine learning models based on analyzing the production telemetry data;
• deploying the first intervention onto an application in a sandbox environment for validation;
• collecting sandbox telemetry data about the application with the first intervention applied in the sandbox environment;
• analyzing the sandbox telemetry data to assess impact of the first intervention in the sandbox environment;
• determining whether performance of the application with the first intervention has improved by at least a predetermined threshold, the predetermined threshold associated with at least one of:
• a latency reduction, a CPU utilization reduction, a memory utilization reduction, a throughput increase, or an error rate reduction;
• in response to determining that the performance of the application with the first intervention has improved by at least a predetermined threshold, deploying the first intervention to the application in the production environment;
• in response to determining that the performance of the application with the first intervention has not improved by the predetermined threshold,
• identifying a second intervention based on the detected performance inefficiencies of the application without the first intervention and the assessed impact of the first intervention;
• and
• deploying the second intervention onto the application in the sandbox environment for validation.