A Survey on Functionality-oriented GUI Testing Empowered by Deep Learning

PhD Qualifying Examination


Title: "A Survey on Functionality-oriented GUI Testing Empowered by Deep 
Learning"

by

Mr. Xiaolei LI


Abstract:

Graphical User Interface (GUI) Testing has become indispensable in software 
testing as an effective method for functionality verification. Through 
interactive GUI testing, software developers can identify discrepancies 
between the expected and actual behavior of the software, allowing them to 
pinpoint and resolve potential defects. However, manually writting test 
cases for GUIs is both time-consuming and prone to human error. To address 
this, automated GUI testing tools have been developed to reduce manual 
effort. Most traditional GUI testing tools rely on coverage-oriented 
metrics, aiming to explore the GUI state space as extensively as possible, 
sometimes overlooking the real business logic of the applications. This lack 
of functionality awareness may lead to a low fault detection rate, compared 
to functionality-oriented testing, which centers around specific target 
functionalities when generating test cases. In recent years, advanced deep 
learning (DL) techniques have been integrated into automated GUI testing 
tools to empower functionality-oriented testing. In this survey, we conduct 
a comprehensive review of DL-boosted functionality-oriented GUI testing 
tools, examining how DL techniques are integrated and the challenges they 
address. Specifically, we discuss two main phases of typical 
functionality-oriented GUI testing tools: target functionality 
identification and guided GUI exploration and a key component: text input 
generation. For each, we identify the critical challenges mitigated by the 
integration of DL techniques and offer insights into opportunities for 
future research in this field.


Date:                   Tuesday, 14 January 2025

Time:                   2:00pm - 4:00pm

Venue:                  Room 3494
                        Lifts 25/26

Committee Members:      Prof. Shing-Chi Cheung (Supervisor)
                        Dr. Yepang Liu (Co-supervisor, SUSTech)
                        Prof. Raymond Wong (Chairperson)
                        Dr. Shuai Wang
                        Dr. Jiasi Shen