Log Event Pairing With Source Code for Faster Debugging
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Solution Overview
Problem
Reviewing log files in chronological order poses challenges during software debugging, requiring manual tagging of events which is time-consuming and error-prone, and existing solutions lack efficient methods for clustering log events without prior knowledge of centroids.
Innovation Solution
The method involves static code analysis to generate vectors, clustering using Gaussian Mixture Model (GMM) for unsupervised learning, and cosine similarity analysis to associate log events with source code segments, generating a Hierarchal Log Event Arrangement (HLEA) model for intuitive visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If log events are reviewed in chronological order, then the log file structure is simple and easy to generate, but it requires manual tagging of events which is time-consuming and error-prone
Solution Approach 1:
The system performs self-service by automatically clustering log events and tagging them with relevant source code information without requiring manual intervention. The Gaussian Mixture Model and cosine similarity analysis enable the system to autonomously organize and annotate log events, eliminating the time-consuming manual tagging process while maintaining simple log file generation.
Solution Approach 2:
The system performs preliminary actions by pre-processing log events through clustering and similarity analysis before they need to be reviewed. By pre-tagging events with source code associations and organizing them into clusters, the system prepares the data in advance, making subsequent debugging activities more efficient without requiring manual tagging at the time of review.
2Loss of information
If manual tagging of log events is performed, then log events can be associated with source code, but the process is error-prone and tedious
Solution Approach 1:
The system replaces the mechanical manual tagging process with automated computational methods. Instead of relying on human operators to manually associate log events with source code, the system uses Gaussian Mixture Model clustering and cosine similarity analysis to automatically perform the association, eliminating human error and the tedious nature of manual tagging while maintaining accurate event-source code relationships.
Solution Approach 2:
The system introduces an intermediary computational layer between log events and source code association. The Gaussian Mixture Model and cosine similarity analysis act as intermediaries that objectively measure and determine the relationships between log events and source code segments, replacing subjective manual judgment with systematic computational analysis that is both accurate and reproducible.
3Adaptability or versatility
If existing clustering methods are used without prior knowledge of centroids, then the system is more adaptable, but existing solutions lack efficient methods for clustering log events
Solution Approach 1:
The system changes the parameters of the clustering approach by using Gaussian Mixture Model with cosine similarity analysis instead of traditional clustering methods. This parameter change enables the system to perform unsupervised clustering without requiring prior knowledge of centroids while maintaining high efficiency, as the GMM approach with appropriate similarity metrics can automatically determine cluster structures from the data itself.
Solution Approach 2:
The system replaces inefficient existing clustering solutions with a more advanced computational approach based on Gaussian Mixture Model and cosine similarity. This substitution maintains the adaptability to cluster without prior knowledge while dramatically improving clustering efficiency through optimized mathematical computations and vector space analysis.
Data Source
AI summary
Access to log event data and corresponding source code is obtained and static code analysis is performed on the source code to produce analysis output. First vectors representing the log event data and second vectors representing the analysis output are generated. A similarity analysis is performed on the first vectors and the second vectors. A probabilistic relevance score associating a given log event with a segment of the source code is determined based on the similarity analysis. A visualization is generated for log events based on the probabilistic relevance score.