Automated Regular Expression Generation for Error Message Clustering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for document classification, such as manual refinement of regular expressions and homogenous cluster generation, are inefficient and resource-intensive, particularly when dealing with large volumes of error messages from software testing, as they require manual inspection and iterative modification.
Innovation Solution
The implementation of automated techniques for generating regular expressions using homogenous clusters, which classify documents and trigger the creation of new clusters, reducing the time spent on grouping error messages by clustering similar messages and generating expressions to match subsequent documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual refinement of regular expressions is used, then classification accuracy can be improved, but time consumption and resource requirements increase significantly
Solution Approach 1:
The system performs preliminary clustering of error messages into homogenous groups before generating regular expressions. This preliminary organization allows the subsequent regular expression generation to work with pre-grouped data, reducing the time required for manual refinement while maintaining classification accuracy through structured processing.
Solution Approach 2:
The system automatically generates regular expressions from clustered error messages without requiring manual inspection or iterative modification. The automated generation process evaluates cluster homogeneity and produces regex patterns autonomously, eliminating the time-consuming manual refinement step while preserving classification quality.
2Measurement precision
If manual inspection of documents in clusters is performed, then cluster homogeneity can be verified, but productivity decreases due to resource-intensive processes
Solution Approach 1:
The system replaces manual inspection with an automated computational process that calculates cluster homogeneity metrics. The homogeneity model automatically evaluates whether clusters meet quality thresholds, substituting the mechanical manual review process with an efficient algorithmic approach that maintains precision while dramatically improving productivity.
Solution Approach 2:
The system transforms the manual inspection process into a parameter-based automated evaluation by defining specific homogeneity metrics and thresholds. This parameterization allows the system to objectively assess cluster quality through computational calculations rather than subjective manual review, enabling scalable processing without sacrificing accuracy.
3Measurement precision
If iterative modification of regular expressions is performed, then matching accuracy improves, but the number of operations and time required increase
Solution Approach 1:
The system performs preliminary clustering and homogeneity evaluation before generating regular expressions, ensuring that the input data is already organized into coherent groups. This preliminary structuring allows the regex generation to achieve high matching accuracy in a single pass without requiring iterative modifications, thereby reducing process complexity.
Solution Approach 2:
The system incorporates feedback mechanisms where the homogeneity model evaluates cluster quality and adjusts the regular expression generation process accordingly. This feedback loop ensures that generated regex patterns achieve high matching accuracy by learning from cluster characteristics, eliminating the need for manual iterative modification while maintaining precision.
Data Source
AI summary
Regular expressions are generated automatically by receiving documents, clustering the documents, and selecting tokens from the documents which occur in each document in the cluster, and generating a regular expression for each cluster of documents using the selected tokens.