LSTM Bug Fix System for Automated Test Failure Root Cause Analysis

The patent replaces manual static review techniques with an automated machine learning system that uses LSTM neural networks to analyze test data and predict root causes. This substitution of mechanical/manual processes with automated intelligent systems directly resolves the contradiction by dramatically improving productivity while reducing time loss through automated failure analysis.

The patent introduces an intermediary machine learning system that acts as a mediator between test failure data and root cause identification. The LSTM-based system processes test data and generates predictions about failure characteristics and sources, serving as an intelligent intermediary that accelerates the analysis process while maintaining accuracy.

The patent merges the machine learning models with the automated test tools into an integrated system. The LSTM-based bug fix system is combined with test execution and data collection capabilities, allowing the system to automatically analyze failures as they occur. This merging resolves the contradiction by achieving high productivity through integration while managing complexity through unified system architecture.

The patent creates a universal bug fix system that performs multiple functions: executing tests, collecting test data, analyzing failures, and predicting root causes. This multi-functional system resolves the contradiction by consolidating various capabilities into a single platform that improves productivity without requiring separate complex systems for each function.