SIP Call Flow Testing Using Learned Reference Traffic
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Solution Overview
Problem
Network equipment vendors face challenges in efficiently and cost-effectively testing new software releases due to manual testing being resource-intensive, time-consuming, and error-prone, leading to incomplete coverage and high upgrade failure rates, which delays the adoption of new features and security updates.
Innovation Solution
Implementing a learning-based automated testing system that generates stochastic traffic models from continuous network traffic sampling to simulate production environments, automates test case generation, and executes regression testing with minimal human intervention, ensuring accurate and efficient verification of network equipment performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual testing is used to test network equipment new releases, then testing can be performed with human oversight, but testing time and costs increase significantly, and coverage remains incomplete
Solution Approach 1:
The testing system performs self-service by automatically generating test cases from production call flow data without requiring manual test plan creation. The system autonomously executes tests, compares results against reference behaviors, and identifies defects, eliminating the need for continuous human intervention while maintaining comprehensive coverage.
Solution Approach 2:
The system creates copies of production call flow data and test scenarios to generate automated test cases. By replicating real-world production traffic patterns and call flows in the testing environment, the system achieves complete coverage without manual effort, as the copied production data serves as the basis for comprehensive automated testing.
2Measurement precision
If manual testing is used to ensure accurate verification of network equipment, then testing precision can be maintained, but resource expenditure increases significantly
Solution Approach 1:
The system implements feedback by continuously comparing test results against reference call flow behaviors captured from production environments. This automated feedback mechanism maintains high testing accuracy by immediately identifying deviations from expected behavior, while eliminating the need for expensive manual verification resources.
Solution Approach 2:
The patent replaces the mechanical system of manual testing with an automated electronic testing system. By substituting human testers with automated software agents that execute test cases and analyze results, the system maintains measurement precision while dramatically reducing resource expenditure on human labor.
3Measurement precision
If lab configuration is manually updated to match production changes, then testing accuracy improves, but time and error rates increase
Solution Approach 1:
The system performs preliminary action by automatically capturing and storing reference call flow behaviors from the production environment before changes are made. When production configurations change, the system proactively updates test configurations using these pre-captured references, ensuring testing accuracy without manual intervention and maintaining high productivity.
Solution Approach 2:
The patent introduces an intermediary automated system that mediates between production environment changes and lab configuration updates. This intermediary automatically translates production configuration changes into corresponding lab test configuration updates, eliminating manual updating efforts while maintaining testing accuracy through systematic configuration synchronization.
4Productivity
If multiple call flows are collapsed into single test cases to reduce overhead, then testing efficiency improves, but test coverage gaps increase
Solution Approach 1:
The system applies segmentation by automatically dividing complex call flows into distinct, atomic test cases based on unique behavior patterns. This segmentation ensures that each test case covers specific aspects of call flow behavior comprehensively, maintaining complete test coverage while improving efficiency through automated case generation and execution.
Data Source
AI summary
The present invention relates to methods, systems, and apparatus for learning-based automated call flow testing. An exemplary method of testing a Session Initiation Protocol (SIP) call processing entity includes the steps of: generating a first test case based on a first reference call flow record for a first SIP call or a trace record for the first SIP call; executing the first test case which includes sending one or more SIP messages to the first SIP call processing entity; generating a first test case call flow record for a first SIP test call corresponding to the first test case; and determining whether or not the first SIP call processing entity failed the first test case based on a comparison of the first test case call flow record and the first reference call flow record.