Automation of Electronic Product Testing Using AI

 

Automation of Electronic Product Testing Using AI

Author: Francisco Prats Quilez

Introduction

This document describes the development of an artificial intelligence (AI)-based solution to automate the process of creating tests to verify the functionality of electronic products, either during their production phase or in prototype verification. This solution aims to expedite product development, reduce human errors, and improve efficiency in quality processes.

Objective

The main objective of this project is to develop a platform that, based on a series of technical documents, can automatically generate Python code to control measurement instruments and execute tests autonomously. Additionally, the goal is to create an infrastructure that efficiently manages the execution of these tests and stores the results in a structured manner.

Development

1. Collection and Processing of Documentation:
• A user interface based on Vue.js has been developed to facilitate the upload of necessary documents, such as the test procedure, product description, and instrument manuals.

• The uploaded documents are processed, and relevant information for generating the test code is extracted.
1. Generation of Instrument Classes:
• A mechanism has been implemented to generate Python classes representing the different measurement instruments.
• An LLM model is used to analyze the instrument manuals and generate the class code, including the necessary functions to control the instruments.
2. Generation of Test Code:
• An LLM model with a large amount of input and output data (tokens) is used to receive all the information provided in the documentation and the different generated instrument classes, and then generate the corresponding code to automate the test.
3. Test Execution Management:
• A NoSQL database has been created to store the test execution sequence and the obtained results.
• A program in LabVIEW has been developed to sequentially execute the tests and store the results in the database.
4. Review and Validation of Tests:
• Test engineers review and validate the generated code before execution to ensure the quality of the tests.

Conclusions

The implementation of this solution has proven successful in automating the test creation process for electronic products. The main benefits obtained are:

• Increased efficiency: Automatic code generation significantly reduces the time needed to create tests.
• Error reduction: Automation minimizes human errors that can occur during manual test creation, especially with future models to come.
• Improved test quality: Using large language models allows for more comprehensive and precise tests.
• Greater flexibility: The platform is highly configurable and can adapt to different types of products and tests.

Future Development

The following improvements are proposed:

• Prompt improvement: Explore techniques to design more effective prompts that allow generating higher quality and more specific code.
• Exhaustive testing: Conduct a wide variety of tests to evaluate the model's performance and identify areas for improvement.
• Integration with other systems: Explore the integration of the platform with other quality management and version control systems, automatically managing communication with the repository.
• Utilization of more powerful LLM models: Leverage advancements in the development of large language models to improve code generation capabilities.
• Continuous learning: Implement continuous learning mechanisms so that the model adapts to new types of tests and product changes.
• Result visualization: Develop visualization tools to facilitate the interpretation of test results.

This solution has the potential to transform the way electronic product testing is conducted, enabling companies to accelerate product development and improve quality.