Application of Machine Learning for GUI Test Automation

Abstract

This paper examines the implementation of machine learning (ML) capabilities in a test automation suite, specifically for automation of graphical user interface (GUI) testing on an electronic design automation (EDA) tool within an integrated circuit (IC) physical design, verification, and implementation flow. We present a case study using existing tests to extract information and propose an ML implementation framework that consists of three modules, which can be adopted as a systematic pattern for test development. Our study focusses on implementation of the third module in this framework. We use the learnings from iterative testing patterns on a set of EDA tools provided by the Calibre RealTime interfaces from Siemens Digital Industries Software. The goal is to reduce human effort in selection and implementation of test cases and reallocate those resources to integral parts of the testing process like, approving and acting. We first establish metrics and variables, utilize VGG16 architecture for image classification and perform training on test data, and achieve an ML model based on accuracy and precision. Using this result, we present ML implementation as part of the script development process and analyze its impact. Based on our results, we conclude the third module of a framework for inclusion of ML in a regression testing suite for GUI test automation.