Dynamic system for active detection and mitigation of anomalies in program code construction interfaces

A computer program product for active detection and mitigation of anomalies in program code construction interfaces, wherein the computer program product provides a proactive plug-in with a dynamic machine learning (ML) anomaly detection model cloud component structured to dynamically detect architectural flaws in program code in real-time in a user coding interface, the computer program product comprising a non-transitory computer-readable storage medium having computer-executable instructions to:

• receive, via an operative communication channel with a user device, a user request to perform defect analysis of a first technology program code being constructed in a current session of a user coding interface;
• activate a machine learning (ML) anomaly detection plug-in component, for dynamically analyzing the first technology program code being constructed in the user coding interface;
• parse, via the ML anomaly detection plug-in component, a plurality of first application session logs associated with the first technology program code in the current session of the user coding interface;
• detect, via the ML anomaly detection plug-in component, a plurality of first classes invoked in the plurality of first application session logs of the first technology program code in the current session of the user coding interface;
• extract, via the ML anomaly detection plug-in component, a plurality of first class files associated with the detected plurality of first classes of the first technology program code from a first repository location;
• construct, via the ML anomaly detection plug-in component, a first execution sequence associated with the first technology program code based on (i) the plurality of first classes and (ii) first execution methods associated with the plurality of first classes;
• transmit, from the ML anomaly detection plug-in component, the plurality of first classes, the first execution methods associated with the plurality of first classes, the first execution sequence and the plurality of first class files to a machine learning (ML) anomaly detection model component and trigger the ML anomaly detection model component to determine anti-patterns associated with the first technology program code;
• determine for each of the plurality of first classes, via the ML anomaly detection model component, an associated application layer of one or more application layers based on at least processing class file code associated with the plurality of first class files;
• identify, via the ML anomaly detection model component, one or more first flaws in the first technology program code based on at least determining anti-patterns associated with the first technology program code;
• transmit, from the ML anomaly detection model component, the identified one or more first flaws in the first technology program code to the ML anomaly detection plug-in component;
• and
• modify, via the ML anomaly detection plug-in component, the user coding interface to embed first interface elements associated with the one or more first flaws in the first technology program code detected by the ML anomaly detection model component.

A method for active detection and mitigation of anomalies in program code construction interfaces, wherein the method provides a proactive plug-in with a dynamic machine learning (ML) anomaly detection model cloud component structured to dynamically detect architectural flaws in program code in real-time in a user coding inter, the method comprising:

• receiving, via an operative communication channel with a user device, a user request to perform defect analysis of a first technology program code being constructed in a current session of a user coding interface;
• activating a machine learning (ML) anomaly detection plug-in component, for dynamically analyzing the first technology program code being constructed in the user coding interface;
• parsing, via the ML anomaly detection plug-in component, a plurality of first application session logs associated with the first technology program code in the current session of the user coding interface;
• detecting, via the ML anomaly detection plug-in component, a plurality of first classes invoked in the plurality of first application session logs of the first technology program code in the current session of the user coding interface;
• extracting, via the ML anomaly detection plug-in component, a plurality of first class files associated with the detected plurality of first classes of the first technology program code from a first repository location;
• constructing, via the ML anomaly detection plug-in component, a first execution sequence associated with the first technology program code based on (i) the plurality of first classes and (ii) first execution methods associated with the plurality of first classes;
• transmitting, from the ML anomaly detection plug-in component, the plurality of first classes, the first execution methods associated with the plurality of first classes, the first execution sequence and the plurality of first class files to a machine learning (ML) anomaly detection model component and trigger the ML anomaly detection model component to determine anti-patterns associated with the first technology program code;
• determining for each of the plurality of first classes, via the ML anomaly detection model component, an associated application layer of one or more application layers based on at least processing class file code associated with the plurality of first class files;
• identifying, via the ML anomaly detection model component, one or more first flaws in the first technology program code based on at least determining anti-patterns associated with the first technology program code;
• transmitting, from the ML anomaly detection model component, the identified one or more first flaws in the first technology program code to the ML anomaly detection plug-in component;
• and
• modifying, via the ML anomaly detection plug-in component, the user coding interface to embed first interface elements associated with the one or more first flaws in the first technology program code detected by the ML anomaly detection model component.