Sequential Set Index for Software Root Cause Location
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Solution Overview
Problem
Manual evaluation of software failures across multiple test cases is time-intensive and inefficient, often leading to repeated efforts due to undiagnosed bugs or environmental issues, causing ripple effects and resource overconsumption.
Innovation Solution
Implementing sequential set index models to classify and organize test cases, analyze root causes, and prioritize issues, thereby automating the identification of root causes and reducing the need for manual debugging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual evaluation of software failures is performed, then thorough analysis of each failure can be achieved, but the process becomes time-intensive and inefficient
Solution Approach 1:
The system performs preliminary actions by automatically analyzing failure data, identifying patterns, and generating root cause hypotheses before human testers intervene. This preliminary automated analysis reduces the time required for manual evaluation while maintaining analysis quality through structured pattern recognition across multiple test cases.
Solution Approach 2:
An intermediary automated analysis system is introduced between the failure data and human testers. This intermediary processes failure data, identifies patterns, and presents synthesized findings to testers, reducing their time burden while preserving thorough analysis through the structured intermediary layer that systematically examines all failure cases.
2Measurement precision
If manual evaluation of each failure is performed, then detailed analysis can be achieved, but resource overconsumption occurs
Solution Approach 1:
The system extracts and separates routine pattern recognition tasks from the overall analysis process, handling them automatically through algorithmic pattern matching. This extraction allows human resources to focus on complex cases while automated systems handle repetitive pattern identification, reducing overall resource consumption while maintaining analysis depth.
Solution Approach 2:
The system creates copies of failure data and analyzes them through multiple automated passes using different pattern recognition algorithms. This copying approach enables comprehensive analysis without requiring proportional increases in human resources, as automated systems can efficiently process multiple data copies simultaneously.
3Productivity
If automated pattern recognition is implemented, then analysis speed improves, but complexity of the system increases
Solution Approach 1:
The automated analysis system is segmented into distinct functional modules: data collection, pattern recognition, root cause identification, and result presentation. This segmentation allows each module to be independently optimized and managed, reducing overall system complexity while maintaining high analysis speed through specialized processing in each segment.
Solution Approach 2:
The system implements universal pattern recognition algorithms that can handle multiple types of failure patterns across different test cases. This multi-functionality reduces system complexity by using a single versatile analysis engine rather than separate specialized tools for each failure type, while maintaining high productivity through efficient universal pattern matching.
Data Source
AI summary
Embodiments are for using sequential set index to determine the root cause location of software issues and problem verification of the software issues. Fail data for a software program is received. The fail data corresponds to a plurality of failed test cases for the software program. It is determined that at least one model in a plurality of models includes at least one case that matches the fail data of the software, each of the plurality of models having one or more cases, the at least one case being in the one or more cases. It is determined that a root cause of the at least one model corresponds to the plurality of failed test cases for the software program.