Bank of America Corporation

Richa Soni

Amit Mishra

Krithika Viswanathan

Silpa Edappilly Santhosh

Kadekoppa Kiran Rohithakshappa

Embodiments of the invention are directed to machine learning-based anomaly detection in program code. The system provides a machine learning (ML) anomaly detection model component structured to detect architectural flaws in program code based on processing application logs associated with technology program code and determining flow sequences between a plurality of layers of code. In particular, the system trains the machine learning (ML) anomaly detection model that is structured to (i) construct a first application layer map based on mapping each of the plurality of first classes associated with the first technology program code to one or more application layers, (ii) determine a first architecture pattern associated with the first technology program code, and (iii) determine whether the first technology program code is associated with an anti-pattern.

Electronic system for machine learning based anomaly detection in program code

Embodiments of the invention are directed to static program code analysis and detection of architectural flaws. The system provides a rule-based anomaly detection engine structured to capture application logs during construction of technology program code and dynamically detect anti-pattern conflicts to remediate defects in the technology program code. In particular, the system receives a request to perform defect analysis of a first technology program code. In response, the system constructs a first layer transition map based on mapping each of a plurality of first classes associated with the first technology program code to one or more application layers. The system may then determine, via an anomaly detection engine component, one or more anomalies associated with the first technology program code.

Electronic system for static program code analysis and detection of architectural flaws

Embodiments of the invention are directed to active detection and mitigation of anomalies in program code construction interfaces. The system 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. In particular, the system activates a machine learning (ML) anomaly detection plug-in for dynamically analyzing the first technology program code being constructed in the user coding interface. Moreover, the system modifies, via the ML anomaly detection plug-in, the user coding interface to embed interface elements associated with the one or more flaws in the first technology program code detected by the ML anomaly detection model cloud component.

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Chennai, India

Richa Soni