Homogeneous Cluster Regex Generation for Error Messages
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing document classification methods are inefficient in automatically generating regular expressions and clustering documents, relying on manual refinement and inspection, which is time-consuming and resource-intensive, especially when dealing with large volumes of error messages from software testing.
Innovation Solution
The method automates regular expression generation using homogenous clusters, classifying documents into existing clusters and creating new clusters by selecting common tokens and generating regular expressions, reducing the need for manual intervention through the use of homogenous cluster classifiers and regex generators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual refinement of regular expressions is used, then classification accuracy is improved, but time consumption and resource usage increase
Solution Approach 1:
The system performs preliminary clustering of error messages into homogenous groups before generating regular expressions. By pre-organizing documents into clusters based on similarity, the system reduces the search space and enables more efficient regex generation, thereby decreasing time consumption while maintaining classification accuracy.
Solution Approach 2:
The system automatically generates regular expressions from clustered error messages without requiring manual refinement. The homogenous cluster classifier and regex generator work together to self-service the classification task, eliminating the need for manual intervention while preserving accuracy through automated pattern extraction.
2Stability of the object's composition
If manual inspection of documents in clusters is performed, then cluster homogeneity is improved, but productivity decreases
Solution Approach 1:
The system replaces manual inspection with an automated homogeneity classifier that uses machine learning models to assess cluster homogeneity. This substitution of mechanical human inspection with an automated computational system maintains cluster quality while dramatically increasing productivity by processing clusters at machine speed.
Solution Approach 2:
The homogeneity classifier provides automated feedback on cluster quality, enabling the system to iteratively refine cluster assignments without manual intervention. This feedback mechanism ensures clusters maintain high homogeneity while the automated process preserves productivity by eliminating manual review bottlenecks.
3Productivity
If automated clustering is implemented, then productivity is improved, but classification precision may deteriorate
Solution Approach 1:
The system segments the classification task into multiple stages: initial automated clustering, homogeneity assessment, and selective refinement. By dividing the process, the system maintains high productivity through automation while ensuring precision through targeted intervention only when necessary, rather than applying uniform manual review to all cases.
Data Source
AI summary
Homogeneous clusters are generated from a first plurality of documents for generation of regular expressions. Documents that share similar characteristics are clustered, and for each cluster, features are generated for use by a homogeneity model to determine a homogeneity score for the cluster. Clusters determined to be homogenous are sent to a regular expression generator.