Riccio, Vincenzo
(2018)
Enhancing Automated GUI Exploration Techniques for Android Mobile Applications.
[Tesi di dottorato]
| Tipologia del documento: |
Tesi di dottorato
|
| Lingua: |
English |
| Titolo: |
Enhancing Automated GUI Exploration Techniques for Android Mobile Applications |
| Autori: |
| Autore | Email |
|---|
| Riccio, Vincenzo | vincenzo.riccio@unina.it |
|
| Data: |
11 Dicembre 2018 |
| Numero di pagine: |
138 |
| Istituzione: |
Università degli Studi di Napoli Federico II |
| Dipartimento: |
Ingegneria Elettrica e delle Tecnologie dell'Informazione |
| Dottorato: |
Information technology and electrical engineering |
| Ciclo di dottorato: |
31 |
| Coordinatore del Corso di dottorato: |
| nome | email |
|---|
| Riccio, Daniele | daniele.riccio@unina.it |
|
| Tutor: |
| nome | email |
|---|
| Fasolino, Anna Rita | [non definito] |
|
| Data: |
11 Dicembre 2018 |
| Numero di pagine: |
138 |
| Parole chiave: |
Android; Software Quality; GUI Testing |
| Settori scientifico-disciplinari del MIUR: |
Area 09 - Ingegneria industriale e dell'informazione > ING-INF/05 - Sistemi di elaborazione delle informazioni |
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| Depositato il: |
22 Gen 2019 22:36 |
| Ultima modifica: |
23 Giu 2020 09:24 |
| URI: |
http://www.fedoa.unina.it/id/eprint/12619 |
Abstract
Mobile software applications ("apps") are used by billions of smartphone owners worldwide. The demand for quality to these apps has grown together with their spread. Therefore, effective techniques and tools are being requested to support developers in mobile app quality engineering activities.
Automation tools can facilitate these activities since they can save humans from routine, time consuming and error prone manual tasks. Automated GUI exploration techniques are widely adopted by researchers and practitioners in the context of mobile apps for supporting critical engineering tasks such as reverse engineering, testing, and network traffic signature generation. These techniques iteratively exercise a running app by exploiting the information that the app exposes at runtime through its GUI to derive the set of input events to be fired.
Although several automated GUI exploration techniques have been proposed in the literature, they suffer from some limitations that may hinder them from a thorough app exploration.
This dissertation proposes two novel solutions that contribute to the literature in Software Engineering towards improving existing automated GUI exploration techniques for mobile software applications.
The former is a fully automated GUI exploration technique that aims to detect issues tied to the app instances lifecycle, a mobile-specific feature that allows users to smoothly navigate through an app and switch between apps. In particular, this technique addresses the issues of crashes and GUI failures, that consists in the manifestation of unexpected GUI states. This work includes two exploratory studies that prove that GUI failures are a widespread problem in the context of mobile apps.
The latter solution is a hybrid exploration technique that combines automated GUI exploration with capture and replay through machine learning. It exploits app-specific knowledge that only human users can provide in order to explore relevant parts of the application that can be reached only by firing complex sequences of input events on specific GUIs and by choosing specific input values.
Both the techniques have been implemented in tools that target the Android Operating System, that is today the world’s most popular mobile operating system. The effectiveness of the proposed techniques is demonstrated through experimental evaluations performed on real mobile apps.
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