Swarm Learning Defect Prevention with Blockchain Edge Computing
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Solution Overview
Problem
Traditional defect management systems are inefficient due to the over-the-wall reactive model, leading to long debug times and increased operational costs, especially due to the presence of false positives that consume valuable troubleshooting time.
Innovation Solution
A decentralized particle swarm model integrated with swarm learning (SL) intelligence, blockchain, and edge computing is used to analyze application failure logs, categorize defects, and generate recommendations for remedying critical software issues, thereby reducing noise and preventing defects throughout the software development lifecycle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional defect tracking tools are used to manage all defects including false positives, then comprehensive defect tracking is achieved, but debug time and operational costs increase significantly
Solution Approach 1:
The system extracts and separates false positive defects from the main defect lifecycle using machine learning classification. False positives are identified early and removed from the traditional defect tracking flow, preventing them from consuming debug time while maintaining tracking of genuine defects.
Solution Approach 2:
The system performs preliminary classification of defects using ML models before they enter the full defect lifecycle. This preliminary action identifies false positives early in the process, allowing them to be disposed of before consuming significant troubleshooting resources.
2Reliability
If traditional over-the-wall reactive defect management is used, then defect tracking is maintained, but turnaround time for defect resolution increases
Solution Approach 1:
The system implements continuous feedback loops where ML models learn from defect outcomes and improve classification accuracy over time. This feedback mechanism enables the system to become increasingly efficient at identifying and routing defects appropriately, accelerating resolution without sacrificing management quality.
Solution Approach 2:
The system replaces the manual, reactive mechanical process of defect triage with automated machine learning-based classification. This substitution dramatically speeds up defect analysis and routing while maintaining or improving management effectiveness.
3Measurement precision
If comprehensive defect analysis is performed on all logged defects, then accurate defect categorization is achieved, but processing overhead and costs increase
Solution Approach 1:
The system applies partial analysis by using ML models to perform quick initial classification of defects. Only defects that require deeper analysis or are classified as genuine issues undergo comprehensive examination, reducing overall processing overhead while maintaining accuracy for critical defects.
Solution Approach 2:
The system changes the parameters of defect analysis by using machine learning models that can quickly evaluate multiple defect characteristics simultaneously. This approach achieves accurate categorization more efficiently than traditional manual analysis methods.
Data Source
AI summary
Aspects of the disclosure relate to s computing system that is configured to use heuristic and/or metaheuristic algorithms based on swarm learning (SL) intelligence frameworks and combine SL with blockchain and edge computing frameworks to provide a technologically efficient, responsive, and/or adaptable solution to detecting and preventing defects in software applications.