Natural Language Incident Reporting for Software Maintenance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Software bugs and performance issues in complex enterprise-level applications can be difficult to identify and resolve efficiently, relying heavily on human expertise, leading to potential delays and errors in incident reporting and resolution, especially in business-critical software.
Innovation Solution
A natural language generator is employed to assist in generating incident reports, providing automated data extraction, report creation, and suggesting resolutions based on historical data, ensuring standardized and accurate documentation and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If software support engineers manually create incident reports and analyze issues, then the reports can be customized and complex analysis can be performed, but the process is time-consuming and subject to human error
Solution Approach 1:
The system enables automated self-service incident report generation by extracting relevant information from incident data and historical records, eliminating the need for manual report creation by support engineers while maintaining accuracy through structured data processing
Solution Approach 2:
The patent replaces the mechanical manual process of incident report creation with an automated computational system that uses natural language processing and data extraction algorithms to generate reports automatically, reducing both time and human error
2Reliability
If incident resolution depends on individual engineer knowledge and experience, then personalized expertise can be applied, but consistency and reliability vary across different engineers
Solution Approach 1:
The system incorporates feedback loops that analyze historical incident data and resolution outcomes, continuously learning from past performance to improve future resolution recommendations while maintaining consistent application of proven solutions
Solution Approach 2:
The patent creates a universal resolution recommendation system that can handle multiple types of incidents and apply standardized best practices across different scenarios, ensuring consistent and reliable outcomes regardless of which engineer is handling the incident
3Reliability
If extensive testing is performed before software release, then fewer bugs reach production, but testing costs and time to market increase
Solution Approach 1:
The system performs preliminary automated analysis of incident data and patterns before full-scale testing, identifying potential issues early in the development cycle that can be addressed before release, reducing the need for extensive post-release troubleshooting
Solution Approach 2:
The patent replaces extensive manual testing with automated testing frameworks and AI-driven bug detection systems that can efficiently identify issues without requiring proportional increases in human testing resources, maintaining quality while reducing time to market
Data Source
AI summary
Techniques and solutions are provided for facilitating the documentation, resolution, and review of software support incidents. In one aspect, a natural language generator is provided with information about a software support incident and creates an incident report. In another aspect, a natural language generator receives information about a software support incident and information about prior software support incidents. The natural language generator proposes solutions to resolve the software support incident. In another aspect, the natural language generator analyzes software support incidents, including resolutions, and provides a summary of software support incidents, and can provide suggested actions to reduce the occurrence of future incidents. The present disclosure also provides techniques for extracting and standardizing incident data, which can improve the quality of results generated by the natural language generator.