Abstract: Systems, computer program products, and methods are described herein for evaluating computing programming code changes using AI to improve computing performance and component consumption. The present invention is configured to identify at least one code commit change; determine whether the at least one code commit change matches at least one runtime restriction statement; classify the at least one code commit change as a restriction code commit change; identify at least one object reference, at least one class, and at least one method associated with the restriction code commit change; initiate a trigger-class method trace module on the restriction code commit change, wherein the trigger-class method trace module generates at least one runtime restriction weight associated with each restriction code commit change; and initiate a trigger flow trace module on the restriction code commit change and generate at least one suggested code commit change.

Abstract: Systems, computer program products, and methods are described herein for code development in a distributed programming environment using programmer telemetry and developer behavior-focus based test suite selection. The present disclosure is configured to capture telemetry data from developer interactions within an integrated development environment (IDE), preprocess and log the telemetry data for further analysis, analyze the data to discern developer behavior and focus levels using machine learning models, generate a focus score quantifying adherence to coding standards and security protocols, select and customize test suites based on the focus score, execute the test suites, and provide feedback to the developer. The system enhances code quality and security by dynamically adapting test suite selection based on real-time developer behavior, ensuring efficient and effective testing processes in a distributed environment.

Abstract: Systems, computer program products, and methods are described herein for evaluating computing programming code changes using AI to improve computing performance and component consumption. The present invention is configured to identify at least one code commit change; determine whether the at least one code commit change matches at least one runtime restriction statement; classify the at least one code commit change as a restriction code commit change; identify at least one object reference, at least one class, and at least one method associated with the restriction code commit change; initiate a trigger-class method trace module on the restriction code commit change, wherein the trigger-class method trace module generates at least one runtime restriction weight associated with each restriction code commit change; and initiate a trigger flow trace module on the restriction code commit change and generate at least one suggested code commit change.