Software Event Discourse Decomposition for Root Cause Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for troubleshooting software issues, such as searching online forums, often result in incomplete answers due to humans tending to identify single root causes for software events that may have multiple causes, making it time-consuming to find relevant discussions and solutions.
Innovation Solution
A system and method that autonomously searches public forums, analyzes natural language conversations, and uses techniques like NLP and machine learning to identify multiple causes of software events by evaluating interrelations between topics, providing immediate recommendations and solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If humans search online forums manually to troubleshoot software issues, then they can find some relevant discussions, but the answers are incomplete because humans tend to identify single root causes while software events may have multiple causes
Solution Approach 1:
The system performs self-service by automatically searching public forums, analyzing conversations, and identifying multiple causes of software events without requiring manual human intervention. The controller autonomously executes the troubleshooting process, gathering information from multiple sources and synthesizing comprehensive cause analysis.
Solution Approach 2:
The patent replaces the mechanical human search and analysis process with an automated computer-based system. The controller uses natural language processing and machine learning algorithms to substitute human cognitive activities with computational processes, enabling faster and more comprehensive analysis of forum discussions.
2Measurement precision
If humans manually search through online forums to identify software event causes, then they can find some solutions, but the process is time-consuming and often yields incomplete results
Solution Approach 1:
The system autonomously performs the complete troubleshooting workflow including searching forums, analyzing conversations, identifying topics, and determining multiple causes. This self-service capability eliminates dependency on manual human effort while maintaining high accuracy through sophisticated natural language processing and interrelation analysis.
Solution Approach 2:
The system performs preliminary actions by automatically searching and analyzing forum conversations before a human user would manually do so. The controller proactively gathers and processes information from multiple public forums, preparing comprehensive cause analysis results in advance, which significantly accelerates the troubleshooting process.
3Loss of information
If a system autonomously analyzes natural language conversations from public forums, then it can identify multiple causes of software events, but the system complexity increases
Solution Approach 1:
The system segments the complex analysis task into distinct functional modules: searching for software events in public forums, identifying topics from conversations, analyzing interrelations between topics, and determining multiple causes. This segmentation allows each module to handle a specific aspect of the analysis, managing overall system complexity through modular architecture.
Solution Approach 2:
The controller acts as an intermediary that coordinates between various analysis components and the user. It manages the complex interactions between topic identification, interrelation analysis, and cause determination modules, providing a simplified interface while handling the sophisticated analysis processes internally.
Data Source
AI summary
A ticket relating to a software event is received in a software development system. One or more public forums on software development is searched for the software event. Two or more topics on the software event are identified from one or more conversations from the one or more public forums that regard the software event. Two or more causes of the software event are determined by analyzing interrelations of the two or more topics.