Machine Learning Test Generation From Natural-Language Requirements
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Solution Overview
Problem
The manual process of coding test scenarios in test automation frameworks is time-consuming and limited to skilled QA engineers, creating bottlenecks in delivering extensive test coverage and affecting code quality and delivery times in complex IT asset projects.
Innovation Solution
Utilizing a fine-tuned large language model (LLM) to generate and execute test scenarios by processing plain English instructions, mapping test steps to API calls, and integrating them into a test automation framework, reducing dependency on skilled QA engineers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual coding of test scenarios is performed by skilled QA engineers, then test scenario quality and accuracy are improved, but time consumption and cost increase significantly
Solution Approach 1:
The patent introduces a machine learning model as an intermediary between the natural language requirement and the test automation framework. The model translates user requirements into structured test scenarios and maps them to appropriate API calls, eliminating the need for manual coding while maintaining quality through learned patterns from training data.
Solution Approach 2:
The patent replaces the mechanical process of manual test scenario coding with an automated machine learning-based system. The ML model automatically generates test scenarios and maps them to framework API calls, substituting human manual effort with an intelligent automated system that learns from training data.
2Reliability
If skilled QA engineers manually create test scenarios, then comprehensive test coverage is achieved, but dependency on skilled personnel creates bottlenecks
Solution Approach 1:
The patent enables the system to serve itself by automatically generating test scenarios from natural language requirements without human intervention. The machine learning model processes requirements, generates test scenarios, and maps them to framework calls autonomously, eliminating bottlenecks caused by dependency on skilled QA engineers.
Solution Approach 2:
The patent changes the fundamental parameter of test scenario creation from manual expert-driven processes to automated ML-driven processes. This parameter change enables scalable test coverage by transforming the creation mechanism from human-limited to computationally scalable.
3Manufacturing precision
If complex test scenarios are manually coded, then accurate test logic is achieved, but code complexity and maintenance difficulty increase
Solution Approach 1:
The patent uses copying by training the machine learning model on existing test scenarios and their corresponding framework API calls. The model learns to replicate successful test logic patterns and applies them to new requirements, generating accurate test scenarios without manual coding while maintaining consistency with proven patterns.
Data Source
AI summary
An apparatus comprises at least one processing device configured to generate a first data structure by parsing a request to generate a given test scenario for testing of an information technology asset, and to process the first data structure utilizing a machine learning model to generate a second data structure comprising a given sequence of test steps for the given test scenario. The at least one processing device is also configured to map the given sequence of test steps in the second data structure to respective application programming interface calls of a test automation framework each associated with a functional code test unit of a test code database of the test automation framework. The at least one processing device is further configured to execute the given test scenario utilizing the mapped application programming interface calls of the test automation framework.