Call Graph Payload Generation for Network DUT Branch Testing
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Solution Overview
Problem
Existing network testing methods are time-consuming, prone to errors, and fail to rigorously test devices under test (DUT) due to the complexity of call graphs, leading to undetected faults and increased risk of network performance issues.
Innovation Solution
A network testing system utilizing an ambassador module that retrieves and analyzes call graphs, generates valid and invalid payloads, and compares responses to expected outcomes, ensuring comprehensive testing and reducing errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual testing methods are used to test devices in network, then testing can be performed with simple tools, but testing is time-consuming and prone to errors
Solution Approach 1:
The system enables self-service testing by automatically generating test cases, payloads, and expected responses without requiring manual intervention. The call graph is automatically analyzed and executed, with results compared against expectations, allowing the testing system to serve itself and eliminate human operators from the testing process.
Solution Approach 2:
The system performs preliminary actions by pre-generating valid and invalid payloads for each decision branch in the call graph before actual testing begins. Expected responses are also prepared in advance, allowing the testing process to proceed efficiently without real-time manual preparation.
2Reliability
If comprehensive testing of all decision branches is performed, then testing coverage is improved, but testing complexity increases
Solution Approach 1:
The testing system segments the call graph into individual decision branches, allowing each branch to be tested independently. This segmentation enables comprehensive coverage of all possible paths while managing complexity by breaking down the overall testing task into smaller, manageable units that can be processed systematically.
Solution Approach 2:
The system performs excessive action by generating both valid and invalid payloads for each decision branch, ensuring that not only normal operation paths are tested but also error handling and edge cases. This approach guarantees comprehensive coverage beyond minimal requirements.
3Measurement precision
If automated payload generation is implemented, then error reduction is achieved, but system complexity increases
Solution Approach 1:
The system introduces an intermediary component that automatically generates payloads and expected responses based on the call graph structure. This intermediary layer handles the complexity of payload generation and response validation, isolating the testing logic from the actual testing execution and improving measurement precision through consistent, error-free automated generation.
Data Source
AI summary
A network testing system includes a non-transitory computer readable medium configured to store instructions thereon; and a processor. The processor is configured to execute the instructions for retrieving a call graph; identifying decision branches within the call graph; and generating a valid request payload for each of the decision branches. The processor is configured to execute the instructions for generating an invalid request payload for each of the decision branches; instructing an orchestrator to test a device under test (DUT) using each of the valid request payload and the invalid request payload; comparing responses received from the DUT to each of the valid request payload and the invalid request payload with expected responses; and determining that the DUT includes a failure in response to a determination that any of the responses received from the DUT fail to match a corresponding expected response of the expected responses.