Publications

Version: 1.1 Approved

LDO-ID 2020-10-16-03

Title Image-based Approaches for Automating Visual Testing of Interactive Web-based Applications

Abstract

Modern visual testing frameworks automate the testing of web-based graphical user interfaces to ensure the absence of functional and visual regressions. They employ common strategies to tackle different testing use-cases. This paper produces an analysis of these strategies; its results highlight that it is not possible to test some kinds of interactive interfaces exhaustively. We propose here a novel image-based framework that combines current techniques with new ones; these leverage Machine Learning and Computer Vision algorithms to analyze screenshots of the interface and prove its correctness. Results suggest that it suffices to automate the verification of interactive interfaces that were not fully testable before; automated tests, developed as a benchmark, present almost no false-positives and high accuracy.

Authors Macchi Federico, Rosin Pierpaolo, Mervi Juan Marcos, Turchet Luca

Type Paper for Seminar/Symposium/Conference

Media Open Innovations Association FRUCT (Finnish-Russian University Cooperation in Telecommunications) Conference (27-29 January 2021, Moscow, Russia)

Web site

https://ieeexplore.ieee.org/document/9347592

Anno 2021

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