Anti-Pattern Detection for Cloud Migration Source Code
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The assessment phase for cloud migration is time-consuming and prone to errors due to the need for manual analysis and the inability of existing tools to automatically learn and detect anti-patterns, leading to inaccurate reports and security concerns with data storage.
Innovation Solution
A system and method for automated anti-pattern detection in cloud migration using a processor-based application assessment subsystem that applies pre-defined rules and machine learning models to detect syntax patterns and generate actionable migration events, enabling efficient and secure cloud migration report generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis is used for cloud migration assessment, then accuracy of anti-pattern detection may be improved, but time consumption increases significantly
Solution Approach 1:
The system employs machine learning models that automatically learn and detect anti-patterns in application source code without requiring manual analysis. The models are trained on historical data and continuously improve their detection capabilities, enabling the system to serve itself in identifying migration issues while maintaining high accuracy and reducing assessment time to minutes.
Solution Approach 2:
The patent replaces the mechanical manual analysis process with an automated machine learning-based system. The ML models process source code, logs, and configuration files automatically, substituting human analysts with intelligent algorithms that can process multiple applications simultaneously without fatigue or time constraints.
2Extent of automation
If conventional rule-based tools are used for assessment, then automation is achieved, but adaptability to learn new anti-patterns is lost
Solution Approach 1:
The system transitions from static pre-defined rules to dynamic machine learning models that can adapt and learn new anti-patterns automatically. The ML models are trained on historical migration data and continuously update their knowledge base, enabling the automated system to recognize emerging anti-patterns and adapt to different application types and migration scenarios.
Solution Approach 2:
The system incorporates feedback mechanisms where detection results and migration outcomes are fed back into the training data for continuous model improvement. This closed-loop approach allows the automated system to learn from past assessments and refine its anti-pattern detection capabilities over time, maintaining both automation and adaptability.
3Productivity
If existing assessment tools are used, then report generation is automated, but security concerns arise from data storage at tool endpoint
Solution Approach 1:
The patent extracts and removes sensitive data from the assessment process by using on-premises or edge-based ML model deployment. Application source code, logs, and configuration files are processed locally without being transmitted to or stored at the tool provider's endpoint, eliminating the security vulnerability while maintaining automated report generation capabilities.
4Measurement precision
If pre-defined rules are customized manually, then assessment accuracy may be improved, but complexity of modification increases
Solution Approach 1:
The system eliminates the need for manual rule customization by employing machine learning models that automatically adapt to specific organization requirements. The models learn from historical data and organizational patterns, automatically configuring detection parameters and rules without requiring manual intervention or complex customization processes.
Solution Approach 2:
The system performs preliminary learning and adaptation during the model training phase using historical assessment data and organizational specifics. This preliminary action pre-configures the detection capabilities before actual assessments begin, eliminating the need for manual rule customization while maintaining high accuracy.
Data Source
AI summary
A system and method for anti-pattern detection for computing application prior to deployment in cloud environment is provided. The present invention provides for applying a pre-defined set of rules on one or more applications source code. The pre-defined set of rules are applied in pre-defined order. Further, applying one or more anti-pattern detection models on one or more applications source code. The anti-pattern detection models are applied for determining correlation between one or more syntax patterns of the application source code and the anti-patterns detection models. Further, detecting anti-patterns associated with the syntax patterns of the application source code based on the pre-defined set of rules and the anti-patterns detection models. The detected anti-patterns represent unique anti-patterns. Lastly, generating a migration actionable event for the application source code based on the detected anti-patterns.