Defect Data Retrieval Order for Adaptive Debugging
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Solution Overview
Problem
Traditional debugging methods require extensive data collection and manual intervention, often gathering unnecessary data, which is inefficient and time-consuming, and do not adapt to the specific needs of different defect categories.
Innovation Solution
A system that learns and optimizes data gathering by determining a defined defect category, generating metadata for data set content and retrieval order through monitoring, and providing an optimal data set for debugging tools, dynamically updating based on usage patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all data is always gathered for all defects to be debugged, then the completeness of defect analysis is improved, but the amount of data and time required for debugging increases
Solution Approach 1:
The patent extracts only the necessary data elements required for debugging a specific defect category, rather than gathering all available data. The system identifies and retrieves only the minimal subset of data elements that are relevant to the defect being analyzed, thereby reducing data volume while maintaining analysis completeness.
Solution Approach 2:
The patent applies local quality by tailoring the data gathering approach to the specific defect category being analyzed. Different defect categories have different data requirements, and the system adapts the data gathering strategy to match the local needs of each defect type, avoiding unnecessary data collection for unrelated defect categories.
2Productivity
If a fixed data set for a given category of defect is gathered, then the efficiency of debugging is improved, but the adaptability to specific defect needs decreases
Solution Approach 1:
The patent implements dynamics by making the data set adaptive rather than fixed. The system continuously learns from actual debugging processes and updates the data gathering strategy in real-time, allowing the fixed data set to evolve into a dynamic, self-improving structure that adapts to specific defect needs while maintaining overall efficiency.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system monitors actual debugging processes and uses this information to refine future data gathering strategies. The learning process incorporates feedback from debugging outcomes to continuously improve the accuracy and relevance of the data sets for different defect categories.
3Measurement precision
If programming and configuration intervention is required to modify the static set of data, then the precision of defect analysis is improved, but the ease of operation decreases
Solution Approach 1:
The patent implements self-service by enabling the system to automatically adjust and optimize its own data gathering strategies without requiring manual programming or configuration intervention. The learning process allows the system to self-optimize the data sets based on actual debugging needs, maintaining high analysis precision while significantly improving operational ease.
Data Source
AI summary
A method, computer program product, and computer system for optimized data gathering for defect analysis. The method includes determining a defined defect category of a reported defect. The method then obtains current data set metadata for the defined defect category, where the data set metadata includes a defined content of the data set and a defined retrieval order of elements of the data set, and wherein the data set metadata is learned from monitoring data set retrieval from an end system during a debugging process. The method loads a data set from an end system for use by a debugging tool, with the data set having the defined content and loaded in the defined order according to the data set metadata.