Metrics-Based Service Testing for Code Path Coverage
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Solution Overview
Problem
Traditional software testing methods often fail to identify infrequently traversed code paths and may miss bugs in these areas, and they lack automation for new code additions, leading to incomplete testing and increased operational costs.
Innovation Solution
The system generates test cases based on metrics data collected during service execution, analyzing log data to identify representative code paths and automatically create test cases that cover a complete set of code paths, including newly added code, thereby ensuring comprehensive testing with minimal operator involvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional software testing methods are used, then testing can be performed with simple processes, but infrequently traversed code paths are not identified and bugs are missed
Solution Approach 1:
The system performs preliminary analysis of log data to identify code paths that are infrequently traversed or not yet tested, before executing tests. This allows the testing system to proactively discover untested code paths and generate appropriate test cases, improving testing completeness without requiring complex manual analysis during test execution
Solution Approach 2:
The system continuously collects log data from service execution, analyzes it to identify code path coverage, and uses this feedback to generate new test cases. This closed-loop feedback mechanism ensures that previously untested or infrequently traversed code paths are systematically identified and tested, improving reliability through iterative refinement of test coverage
Solution Approach 3:
The testing system automatically generates test cases based on analysis of service log data without requiring manual intervention. The system serves itself by using its own operational data to create its own test cases, eliminating the need for complex external testing processes while maintaining comprehensive code path coverage
2Extent of automation
If manual testing processes are used, then testing can be performed with simple setup, but operator involvement is high and automation is lacking
Solution Approach 1:
The testing system automatically generates test cases by analyzing its own service log data without requiring manual operator involvement. The system extracts code path information from logs, identifies untested paths, and autonomously creates test cases to cover them, achieving high automation while keeping the system relatively simple by using existing operational data
Solution Approach 2:
The testing system serves multiple functions: it operates the service, collects log data, analyzes code path coverage, and generates test cases all within a single integrated system. This multi-functionality reduces the need for separate manual testing processes and external tools, increasing automation extent without proportionally increasing system complexity
3Reliability
If comprehensive code path testing is implemented, then all code paths including infrequently traversed ones are tested, but testing time and resources increase
Solution Approach 1:
The system performs preliminary analysis of log data to identify specific code paths that need testing, rather than exhaustively testing all possible code paths. By pre-identifying infrequently traversed or untested paths from actual service logs, the system focuses testing efforts only on areas that need improvement, reducing overall testing time while maintaining high bug detection capability
Solution Approach 2:
The system implements testing focused specifically on infrequently traversed code paths identified from log data, rather than attempting to test every possible code path equally. This partial action approach targets the most critical untested areas based on actual service usage patterns, achieving comprehensive bug detection in high-risk areas without the time cost of exhaustive testing of all possible paths
4Adaptability or versatility
If traditional testing methods are used, then testing processes are simple to implement, but they lack adaptability to new code additions
Solution Approach 1:
The system continuously analyzes log data to identify newly added code paths before they are tested. By performing preliminary analysis of service execution logs, the system detects new code additions and automatically generates test cases for them, ensuring adaptability to new code without requiring manual updates to testing processes or complex change management systems
Solution Approach 2:
The testing system uses feedback from service log data to automatically adapt to new code additions. When new code is deployed and executes services, the logs capture this new execution paths, which the analysis component detects and uses to generate new test cases. This automatic feedback loop provides adaptability to new code while keeping the system relatively simple by leveraging existing operational data
Data Source
AI summary
Techniques are described for determining test cases to test a service, such as a service to manage a purchase contract in an e-commerce environment. Log data may be generated during execution of the service to process requests, the log data indicating a plurality of code segments that executed during the execution of the service. The log data may be analyzed to generate metrics data comprising a plurality of records corresponding to the processed requests. A record may include any number of counters that each indicates whether, or how many times, a particular code segment executed during processing of a request. The metrics data may be analyzed to identify a subset of records comprising representative combinations of the counters in the metrics data. The requests corresponding to one or more of the subset of records may be replayed to test the service.