Error Log Matching With LLMs for Faster Software Debugging
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Solution Overview
Problem
Debugging software with large and complex source code is time- and resource-consuming, especially for non-developer users, due to the complexity of parsing and understanding error logs, and identifying effective solutions for root causes.
Innovation Solution
A software debugging system that leverages large language models (LLMs) and entity matching models to generate embeddings for error logs, match historical errors, and propose solutions based on historical data, enabling efficient identification and mitigation of errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual debugging methods are used, then developers can identify and resolve errors, but the process becomes time- and resource-consuming especially for non-developer users
Solution Approach 1:
The patent introduces an intermediary system comprising error log analysis components, embedding generation models, and solution recommendation engines that mediate between the error logs and users. This intermediary automatically processes error logs, generates embeddings, identifies patterns, and recommends solutions, eliminating the need for users to manually parse complex error logs while maintaining high accuracy in error identification and resolution.
2Measurement precision
If complex error log parsing is performed manually, then accurate error identification is achieved, but the complexity burden falls heavily on non-expert users
Solution Approach 1:
The system enables self-service debugging by automatically performing error log analysis, embedding generation, pattern recognition, and solution recommendation without requiring user expertise. The error processing component autonomously parses logs, the embedding model self-generates feature representations, and the solution recommendation engine automatically identifies and proposes fixes, allowing non-expert users to resolve errors with minimal effort while maintaining high accuracy.
3Reliability
If comprehensive error log analysis is performed, then root causes are accurately identified, but the computational resources and time required increase significantly
Solution Approach 1:
The system performs preliminary actions by pre-processing error logs into standardized formats, pre-generating embeddings for common error patterns, and pre-building knowledge bases of known errors and solutions. When a new error occurs, the system compares it against pre-processed data and known patterns, significantly reducing the computational resources and time required for real-time analysis while maintaining high root cause identification accuracy.
Data Source
AI summary
Methods, systems, and computer-readable storage media for a software debugging system that leverages a LLM in combination with an entity matching model to propose solutions for mitigating errors. In some examples, the LLM is used to generate embeddings that are used to identify two or more matched historical errors. The matched historical errors are potential matches to a current error that needs to be resolved. The entity matching model is used to determine a matched historical error from the two or more matched historical errors. The LLM is also used to generate a proposed solution to the current error based on a historical solution to the matched historical error. The proposed solution can be executed to resolve the current error.