ML Anomaly Detection in Coding Interfaces

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Solution Overview

Problem

Conventional program code testing methods are inadequate for real-time detection and mitigation of anomalies, particularly in large-scale code development where architectural flaws can lead to malfunctioning programs and devices, and are not effective in identifying root causes of inaccuracies or cascading defects.

Innovation Solution

A dynamic system employing a machine learning (ML) anomaly detection model integrated into a user coding interface to detect architectural flaws in real-time by analyzing application session logs, constructing execution sequences, and identifying anti-patterns, allowing for immediate remediation and prevention of compound defects.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If conventional program code testing methods are used, then code development can proceed without real-time monitoring, but architectural flaws and anomalies cannot be detected until later stages leading to cascading defects

Engineering Contradiction:

Improvecode qualityVSAvoidtime to detect defects

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary analysis of program code by inserting instrumentation code during the coding phase to capture execution data, variable states, and control flow information. This preliminary action enables anomaly detection to occur during code construction rather than after deployment, preventing cascading defects before they propagate through the system.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements continuous feedback loops where execution data is captured, analyzed against expected behavior models, and used to generate real-time alerts when anomalies are detected. This feedback mechanism allows developers to immediately correct architectural flaws during the coding session, maintaining code quality without delayed discovery.

Inventive Principle:

Principle #23Feedback

2Measurement precision

If real-time anomaly detection is implemented using ML models, then architectural flaws can be identified immediately, but system complexity and computational resources increase

Engineering Contradiction:

Improveanomaly detection accuracyVSAvoidsystem architecture

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system introduces an intermediary anomaly detection engine that sits between the program execution and the developer interface. This intermediary component captures execution data through instrumentation, processes it through ML models to detect anomalies, and presents results to developers. This mediation layer isolates the complexity of ML operations from the core coding environment while maintaining high detection accuracy.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The anomaly detection system is segmented into modular components: instrumentation code insertion, execution data capture, ML-based analysis engine, and results presentation layer. Each component operates independently and can be configured separately, reducing overall system complexity while enabling precise anomaly detection through specialized processing in each segment.

Inventive Principle:

Principle #1Segmentation

3Reliability

If comprehensive code analysis is performed to detect all architectural flaws, then code quality improves, but development productivity decreases due to constant monitoring

Engineering Contradiction:

Improvecode qualityVSAvoiddevelopment speed

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system applies partial analysis by focusing instrumentation and anomaly detection on critical code paths, architectural boundaries, and high-risk operations rather than uniformly analyzing every line of code. This selective approach maintains code quality for essential architectural elements while minimizing the productivity impact of continuous monitoring.

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The anomaly detection system operates periodically at strategic points in the coding process, such as when architectural patterns are invoked or when execution reaches critical sections, rather than continuously monitoring every operation. This periodic action maintains code quality assurance while allowing developers to work without constant interruption, preserving development speed.

Inventive Principle:

Principle #19Periodic action

Data Source

PatentUS11537502B1Dynamic system for active detection and mitigation of anomalies in program code construction interfaces

Publication Date: 2022.12.27 BANK OF AMERICA CORP

AI summary

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.