LLM Test Sequence Generation for Faster DUT Validation
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Solution Overview
Problem
Test engineers face high overhead costs in time, training, and expertise due to the need to interact with multiple disparate software systems and require specialized knowledge across various roles in developing test processes for devices under test (DUTs), leading to extended time to market.
Innovation Solution
A generative AI-based system, utilizing a large language model (LLM), generates a test sequence from documentation associated with the DUT, including operating ranges and available tests, to streamline the test process by integrating various tools and reducing the need for extensive expertise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple disparate software systems are used to support test process development, then test functionality and capabilities are improved, but device complexity and overhead costs increase
Solution Approach 1:
The patent combines multiple disparate software systems into a single integrated test process development platform. The system unifies test design, test execution, and result analysis functionalities that previously required separate tools, thereby reducing system complexity while maintaining comprehensive test capabilities.
Solution Approach 2:
The invention creates a universal test process development system that performs multiple functions including test sequence generation, test case management, and result analysis. This multi-functional platform replaces the need for multiple specialized tools, reducing overhead costs while providing versatile test support.
2Reliability
If specialized expertise across multiple roles is required for test process development, then test quality and comprehensiveness are improved, but loss of time and training costs increase
Solution Approach 1:
The system enables automated test sequence generation where the test process development system itself performs tasks that previously required specialized human expertise. The automated generation of test sequences from requirements reduces dependency on highly skilled test engineers while maintaining test quality.
Solution Approach 2:
The system performs preliminary test sequence generation and validation automatically before human review, preparing high-quality test cases in advance. This preliminary automated action reduces the time required for manual test design while maintaining comprehensive test coverage.
3Reliability
If comprehensive test process development is performed manually, then test thoroughness is improved, but productivity and time to market worsen
Solution Approach 1:
The patent replaces manual mechanical test design processes with automated computational systems. The system automatically generates test sequences, validates test cases, and manages test execution, substituting human manual efforts with automated processes that maintain thoroughness while dramatically improving productivity.
Solution Approach 2:
The system changes the parameters of test development by using automated algorithms to generate test sequences based on input requirements. This transformation from manual parameter specification to automated parameter generation maintains test thoroughness while accelerating development speed.
Data Source
AI summary
Apparatuses, systems, and methods for test sequence generation to validate a device under test (DUT) can include generating a structured set of test sequence inputs and performing a large language model (LLM) call using the structured set of test sequence inputs. The structured set of test sequence inputs can include parameters associated with the DUT, a list of available tests associated with the DUT, instructions for an LLM to query a database to retrieve test sequence information associated with the DUT, and/or an output structure. The LLM call can be used to generate the test sequence for validating the DUT based on the generated structured set of test sequence inputs.