Log File Error Resolution Using NLP and mSIF Matching
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Solution Overview
Problem
The manual scanning of log files by subject matter experts in the DevOps process leads to a bottleneck in automation, resulting in reduced productivity and agility due to the high degree of heterogeneity and reliance on SMEs for error solutions, which often requires rework.
Innovation Solution
A processor-implemented method and system using Natural Language Processing (NLP) techniques, including tokenization, stop word removal, and a modified Smooth Inverse Frequency (mSIF) model, to automatically identify solutions for errors in log files from different execution environments, standardizing and deduplicating error records, and predicting solutions using a knowledge base.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual scanning of log files by subject matter experts is used, then accurate error identification can be achieved, but productivity is reduced and automation is bottlenecked
Solution Approach 1:
The system enables self-service log analysis by automatically processing log files through NLP techniques. The automated pipeline performs tokenization, stop word removal, and cosine similarity matching without requiring manual SME intervention, thus maintaining accuracy while significantly improving productivity
Solution Approach 2:
The manual mechanical process of SME scanning is replaced with an automated computational system using NLP libraries and vector models. The system substitutes human cognitive processing with algorithmic text analysis, achieving both automation and maintained accuracy through cosine similarity comparison
2Ease of operation
If manual error analysis by SMEs is performed, then solutions can be provided, but rework occurs and heterogeneity increases
Solution Approach 1:
The system applies homogeneous processing rules to all log files regardless of source or format. By using consistent NLP tokenization, stop word removal, and cosine similarity thresholds, the system produces uniform error classifications and solutions, eliminating the heterogeneity introduced by different SME approaches
Solution Approach 2:
The automated system provides universal error analysis capability across multiple execution environments and log formats. The same NLP pipeline and vector comparison mechanism handles diverse input types, ensuring consistent solution quality without relying on individual SME expertise variations
3Quantity of substance
If thousands of log files are generated daily from automated testcases, then comprehensive testing coverage is achieved, but manual consolidation becomes a huge bottleneck
Solution Approach 1:
The system segments the large volume of log files into individual processable units. Each log file is independently tokenized and converted to error sentence vectors, allowing parallel processing of thousands of files simultaneously, thus handling comprehensive testing coverage without time loss
Solution Approach 2:
The system creates vector representations (copies) of error sentences from the original log files. These vector copies enable efficient similarity comparison without repeatedly processing the full text of thousands of log files, dramatically reducing consolidation time while maintaining analysis completeness
Data Source
AI summary
In the DevOps process, testing teams use automated testcases to test a product in a regression testing running daily, which generate thousands of log files per run, in various distributed environment with different formats. Solutions to errors depend on SMEs and the impact of solution leads to rework of defect/errors and high degree of heterogeneity. This leads to huge bottleneck in automation of DevOps process leading to loss of productivity and agility. Present disclosure addresses the challenges by providing systems and methods that auto capture the log files of different formats efficiently in a scalable, extendible, and plug-able way. The system then mines and parses the log files based on given identifiers to standardize and de-duplicate to create unique error records with detail description including cause, position, module, timestamp, etc. The system predicts the solutions leveraging the database containing solutions and errors by using a modified Smooth Inverse Frequency technique.