Proactive Defect Detection via Sentiment and NLP Analysis
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Solution Overview
Problem
Existing digital applications often face defects post-deployment, leading to user frustration and reputation damage, as existing defect detection and resolution methods are inadequate in enterprise environments with unknown or large user groups, where manual intervention is limited and social feedback can be damaging.
Innovation Solution
A multi-data analysis based proactive defect detection and resolution system utilizing sentiment analysis, natural language processing, and event flow data to identify potential defects and trigger resolution processes, linking consumer feedback with application development data to generate defect tickets and modify application code.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual defect detection methods are used in enterprise environments with large user groups, then human intervention can identify issues, but the scale and anonymity of users make manual intervention limited and ineffective
Solution Approach 1:
The system enables self-service defect detection by automatically analyzing user feedback, event flow data, and application code without requiring manual human intervention. The proactive defect detection system autonomously identifies potential defects, generates defect tickets, and triggers resolution processes, allowing the system to serve itself in detecting and resolving defects at scale.
Solution Approach 2:
The patent replaces manual mechanical defect detection methods with automated computational analysis. Instead of human reviewers manually examining user feedback and identifying defects, the system uses automated text analytics, sentiment analysis, and event flow pattern recognition to detect defects programmatically, substituting human cognitive processes with computational algorithms.
2Reliability
If social feedback from users is monitored for defect detection, then user opinions can be captured, but negative feedback can damage reputation and cause user frustration
Solution Approach 1:
The system performs preliminary defect detection by proactively analyzing user feedback and event flow data before defects manifest as widespread user frustration or reputation damage. By identifying potential defects early through automated monitoring and analysis, the system can trigger resolution processes beforehand, preventing the harmful effects of defective applications from reaching users and damaging reputation.
Solution Approach 2:
The system implements a closed-loop feedback mechanism where user feedback is continuously monitored, analyzed for potential defects, and used to trigger automated resolution processes. The feedback from defect detection feeds back into the application lifecycle management system, which automatically generates defect tickets and initiates resolution, creating a continuous improvement cycle that prevents reputation damage by addressing issues before they escalate.
3Productivity
If automated defect detection systems are implemented, then defect detection efficiency improves, but the system requires integration of multiple data sources and complex analysis processes
Solution Approach 1:
The proactive defect detection system is designed as a multi-functional platform that simultaneously handles multiple data sources including user feedback, event flow data, application code, and operational metrics. The system performs multiple functions including text analytics, sentiment analysis, pattern recognition, defect identification, ticket generation, and resolution triggering, all within a single integrated platform that manages complexity through unified architecture.
Solution Approach 2:
The system segments the complex defect detection process into distinct analytical layers: user feedback analysis layer, event flow data analysis layer, code analysis layer, and defect synthesis layer. Each layer processes specific types of data independently using specialized algorithms, then integrates findings at the synthesis layer to identify potential defects, managing complexity through modular segmentation of analytical functions.
Data Source
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AI summary
Multi-data analysis based proactive defect detection and resolution may include analyzing operational data for an application to determine whether a functionality related to the application is below a predetermined threshold associated with the functionality related to the application, and based on the analysis, generating an indication to perform defect analysis related to the functionality related to the application. A sentiment analysis may be performed on consumer data related to the application to determine a sentiment of the consumer data related to the application, and a natural language processing (NLP) analysis may be performed on the consumer data related to the application to determine a function associated with a negative sentiment. Application code and process data related to the application may be analyzed to determine a defect associated with the application. Further, a code of the application may be modified to correct the defect associated with the application.