Microservices Code Similarity Analysis for Risk Identification
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Solution Overview
Problem
In microservices architecture, managing and maintaining complex environments with hundreds of moving parts, coupled with the challenge of identifying and addressing risky code that is inefficient, error-prone, or causes application failures, is complicated by the lack of tools that account for code structure and duplication.
Innovation Solution
A system that identifies risky code in microservices by determining a similarity score between code segments across different microservices, using unified modeling language to generate a connected graph and a diff utility to recommend modifications made to similar code segments, thereby reducing engineering resources and improving customer experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If code duplication is avoided (DRY principle), then code quality improves, but coupling between microservices increases leading to distributed monolith
Solution Approach 1:
The patent segments the monolithic codebase into independent microservices, allowing each service to have its own code segments. This segmentation enables teams to work independently on different services without creating tight coupling, while still allowing selective code sharing when beneficial.
Solution Approach 2:
The patent applies local quality by allowing code duplication only in specific local contexts where it provides value, rather than enforcing global DRY principles. The system identifies similar code segments across microservices and allows selective sharing, maintaining code quality without forcing uniformity across the entire system.
2Adaptability or versatility
If microservices are decomposed into smaller services, then modularity and independent deployment improve, but managing complex environments with hundreds of moving parts becomes difficult
Solution Approach 1:
The patent creates a universal code management system that works across all microservices regardless of their specific function or team ownership. The system provides multi-functional capabilities including code segment identification, similarity analysis, and recommendation generation that apply uniformly across the entire microservices ecosystem.
Solution Approach 2:
The patent introduces an intermediary code management system that mediates between individual microservices and their development teams. This intermediary layer provides centralized visibility and control over code segments across all services, making it easier to manage complexity without sacrificing the independence of individual services.
3Reliability
If standard code analysis tools are used, then code efficiency can be identified, but tools do not account for code structure or duplication in microservices architecture
Solution Approach 1:
The patent changes the parameters of code analysis by introducing similarity scoring metrics specifically designed for microservices architecture. Instead of using standard code quality metrics, the system analyzes code segments based on their similarity to other segments across microservices, enabling identification of duplication patterns and refactoring opportunities.
Data Source
AI summary
Identifying risky code in microservices is described. A system identifies a modification to code segments in an application. The system determines a segments similarity score based on comparing the code segments, which are in a microservice of the application, against other code segments, which are in another microservice of the application. The system determines whether the segments similarity score satisfies a segments similarity threshold. The system outputs a recommendation to make the modification, which was made to the code segments in the microservice of the application, to the other code segments in the other microservice in the application, in response to a determination that the segment similarity score satisfies the similarity threshold.