Chatbot Diagnostics for Application Performance Issues
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Solution Overview
Problem
Existing software applications often encounter performance issues that are not fully diagnosed due to limited crash reports, which do not capture user-provided information essential for resolving issues, and lack contextual data from the user device or network.
Innovation Solution
A diagnostics chatbot on the user device interacts with users to gather performance issue information through conversational prompts and retrieves contextual data from the device and network, packaging this data for analysis to diagnose application and network problems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If crash reports are used to diagnose application performance issues, then some performance issues can be identified, but user-provided information and contextual data are not captured
Solution Approach 1:
A chatbot serves as an intermediary between the user and the diagnosis system. The chatbot collects user-provided information through conversational interactions and retrieves contextual data from the device, then packages this information for analysis. This intermediary approach captures comprehensive information without requiring direct complex system integration.
Solution Approach 2:
The diagnosis system enables self-service by allowing users to provide information through natural conversation with the chatbot. Users can describe performance issues in their own words while the system automatically retrieves relevant contextual data from the device and network, reducing the need for manual technical diagnostics.
2Measurement precision
If comprehensive data collection is implemented to improve diagnosis accuracy, then application reliability improves, but data processing complexity increases
Solution Approach 1:
The data collection process is segmented into distinct components: user-provided information collected through chatbot conversations, device contextual data retrieved automatically, and network contextual data obtained separately. This segmentation allows each data source to be processed independently and then integrated, improving diagnosis accuracy while managing complexity through modular organization.
Solution Approach 2:
The chatbot serves multiple functions: collecting user-provided information about performance issues, retrieving device contextual data, obtaining network contextual data, and packaging all information for analysis. This multi-functional approach consolidates data collection tasks into a single versatile component, improving measurement precision without proportionally increasing system complexity.
Data Source
AI summary
A chatbot executes on a user device to gather performance data associated with an application on the user device. During a diagnostics chat with a user, the chatbot receives a plurality of answers from a user of the user device to a plurality of prompts sequentially output by the chatbot to the user regarding a performance issue of an application on the user device. The chatbot also retrieves, from the user device, contextual data of the application during the performance issue. The chatbot packages the answers received from the user and the retrieved contextual data to enable diagnosis of the performance issue of the application.