Automated Log Error Resolution via ML Classification
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Solution Overview
Problem
The conventional process of fixing errors in software applications by production support teams is time-consuming, as it involves extensive manual effort to identify and resolve errors in log files, particularly for commonly occurring errors that require simple fixes.
Innovation Solution
A method and system that utilize machine learning techniques to automatically identify and resolve common errors in log files by classifying errors into categories and using trained models to apply fixes without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual identification and resolution of errors by production support team is used, then error resolution can be performed with current capabilities, but the process consumes excessive time and resources
Solution Approach 1:
The system enables self-service by automatically identifying and resolving errors through machine learning models without requiring continuous manual intervention from the production support team. The automated error resolution system processes log files, identifies errors, classifies them into categories, and applies appropriate fixes autonomously, allowing the system to serve itself rather than requiring constant human assistance.
Solution Approach 2:
The patent replaces the mechanical manual process of error identification and resolution with an automated machine learning-based system. The machine learning models automatically analyze log files, detect error patterns, and apply fixes, substituting the human-operated mechanical process with an intelligent automated system that operates continuously without fatigue.
2Reliability
If production support team focuses on all errors manually, then all errors can be resolved, but complex errors require more time while simple errors waste valuable resources
Solution Approach 1:
The system segments errors into different categories (first category and second category) based on their complexity and resolution requirements. This segmentation allows the system to apply different handling strategies: automated resolution for simple errors in the first category and manual intervention for complex errors in the second category, optimizing both completeness and efficiency.
Solution Approach 2:
The patent applies local quality by providing differentiated treatment to different types of errors. Simple errors receive automated resolution with standard procedures, while complex errors receive specialized manual attention. This localized approach ensures that resources are allocated efficiently based on the specific characteristics and requirements of each error type.
3Loss of time
If automated error resolution system is implemented, then time consumption is reduced, but system complexity increases with multiple trained models
Solution Approach 1:
The system achieves universality by implementing a multi-functional platform that handles multiple tasks: log file processing, error identification, error classification, and automated resolution. The machine learning models are trained to perform various functions within the same system architecture, reducing the need for separate specialized systems and managing complexity through integrated design.
Data Source
AI summary
A method and a system for automatically identifying and resolving at least one error in at least one log file are provided. The method includes receiving, via a communication interface, the at least one log file from at least one log store. The method further includes identifying, using a first trained model, the at least one error in the at least one log file. Next, the method includes classifying the at least one error into a first category of errors and a second category of errors. Thereafter, the method includes automatically resolving, using a second trained model, the first category of errors in the at least one log file.