REST API Testcase Generation Using Schema Inference and Semantic Profiles
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Solution Overview
Problem
Existing methods for generating test data and testcases for REST APIs in modern application architectures face challenges due to the need for accurate modeling, high technical thresholds, high application costs, and the exponential increase in the number of testcases and test data, along with difficulties in achieving realistic, high-quality, and diverse data validation.
Innovation Solution
A system and method that utilizes a processing subsystem to automatically generate test data and testcases by identifying variable names and types, generating schemas, applying semantic profiles, and organizing test cases into suites using trace collection, schema inference, semantic profile generation, and test execution modules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If model-based or formal-based methods are used for automatic test case generation, then testing efficiency is improved, but technical threshold and application cost increase significantly
Solution Approach 1:
The patent replaces complex model-based or formal-based methods with a heuristic-driven automated system that uses trace collection, schema inference, and semantic profile generation. This substitution eliminates the need for manual modeling while maintaining automatic test case generation capability, thereby reducing technical threshold without sacrificing productivity
Solution Approach 2:
The system performs self-service by automatically collecting traces, inferring schemas, generating semantic profiles, and creating test cases without requiring external modeling input. The system serves itself by extracting all necessary information directly from API requests and responses, eliminating the need for users to provide complex models or formal descriptions
2Reliability
If comprehensive test cases are generated for REST APIs, then testing coverage is improved, but number of testcases and test data increases exponentially
Solution Approach 1:
The patent applies local quality by generating test cases tailored to specific semantic profiles and variable types rather than creating exhaustive combinations of all possible inputs. Each test case is locally optimized for its specific semantic profile (e.g., email validation, date format) rather than treating all inputs uniformly, thereby maintaining high coverage without exponential growth
Solution Approach 2:
The system changes parameters by inferring semantic profiles from trace data and using these profiles to generate test cases with appropriate constraints. Instead of treating all variables uniformly, the system adapts test case generation to the specific semantic characteristics of each variable (e.g., required formats, validation rules), reducing the total number of test cases needed while maintaining comprehensive coverage
3Manufacturing precision
If realistic and diverse test data is generated, then test quality is improved, but data validation complexity increases
Solution Approach 1:
The patent applies preliminary action by generating semantic profiles that include validation constraints before test cases are executed. The schema inference module analyzes trace data and pre-determines the expected formats and validation rules for each variable, so that when test cases are generated, the validation logic is already in place and simplified, rather than requiring complex validation to be written during test execution
Data Source
AI summary
A system for generating test data and testcases to test a set of application programming interface (API) is disclosed. The system includes a processing subsystem, which includes a trace collection module identifies the variable names and the corresponding the variable types to generate the schema, a schema inference module to generate and recognize a schema of the application programming interface (API), a semantic profile generation module includes a predefined set of semantic profiles, defines a custom semantic profile, divides the variable name into individual components, expand a plurality of abbreviated components to complete the filler words, a test case generation module generates a plurality of testcases using a data generated by the semantic profile, organize and group the generated test cases, a test execution and learning module executes the generated testcases and collect results, enables the user to override the generated test case.