Enhanced Error Messages From Stack Trace Parsing
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Solution Overview
Problem
Conventional error messages generated by systems are often difficult for average users to understand due to the inclusion of long stack traces, requiring tribal knowledge to locate helpful information and manually traverse through technical details.
Innovation Solution
A method and system for generating enhanced error messages that utilize regular expression patterns to parse raw error messages, converting them into user-friendly natural language sentences, including titles, descriptions, and recommendations, with optional visual representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If raw error messages with stack traces are generated by the system, then complete technical information is provided, but the error messages become difficult for average users to understand
Solution Approach 1:
The error message is segmented into distinct components: a user-friendly natural language description, a technical stack trace section, and structured error data. This segmentation allows the system to preserve complete technical information while presenting a simplified view to users, resolving the contradiction between information completeness and understandability.
Solution Approach 2:
The patent introduces an intermediary error message generation system that translates raw technical error data into user-friendly natural language descriptions. This intermediary layer processes the stack trace and error information, presenting it in an accessible format while maintaining the underlying technical completeness for those who need it.
2Reliability
If detailed stack traces are included in error messages, then complete diagnostic information is available, but users require expert knowledge to locate helpful information
Solution Approach 1:
The patent extracts the essential diagnostic information from the complex stack trace and presents it in a simplified format. The system identifies and extracts key error causes, locations, and suggestions, separating them from the verbose stack trace details. This extraction maintains diagnostic accuracy while reducing the complexity users must navigate.
Solution Approach 2:
Different portions of the error message are given different qualities: the user-facing portion uses simple natural language with clear structure, while the technical portion retains the full stack trace for experts. This local quality differentiation allows the same error message to serve both novice users and experienced developers effectively.
3Loss of information
If conventional error messages with stack traces are used, then all technical details are preserved, but users must manually traverse through technical details to resolve errors
Solution Approach 1:
The system performs preliminary processing of error messages by automatically generating user-friendly descriptions, identifying key error locations, and formulating resolution suggestions before the user sees the error. This preliminary action saves users time by presenting the most relevant information first, while the complete technical details remain available for reference.
Solution Approach 2:
The error message system provides structured feedback to users with clear suggestions for resolution based on the detected error type. This feedback mechanism guides users through the resolution process efficiently, reducing the time needed to interpret and act on error information while maintaining access to complete technical details.
Data Source
AI summary
Systems and methods for generating an enhanced error message are provided. An example method includes: receiving one or more raw error messages. The one or more raw error messages include one or more stack traces. The method further includes matching at least one raw error message of the one or more raw error messages to one or more error rules from a plurality of error rules. The one or more error rules include regular expression patterns. The method further includes parsing the at least one raw error message, based on the one or more matched error rules from the plurality of error rules; and generating one or more enhanced error messages, based on the at least one parsed raw error messages. The one or more enhanced error messages include one or more natural language sentences. The method further includes embedding the one or more enhanced error messages into a website.