Workshop

Xiao ChenOrganizer

Monash University

Guozhu MengOrganizer

Institute of Information Engineering, Chinese Academy of Sciences

China

Jacques Klein

University of Luxembourg, Luxembourg

Luxembourg

Sam Malek

University of California at Irvine, USA

United States

Workshop

Abstract: Achieving successful technology adoption in industrial practices has often been an important goal for both academic and industrial researchers. However, it is generally challenging for researchers to successfully transfer research results into industrial practices or collaborate with industrial practitioners to produce solutions for successful adoption in industrial practices. This talk presents experiences and lessons learned from years of collaborating with industrial practitioners on successfully developing and deploying solutions for mobile app testing and analysis in the context of major companies such as Tencent (WeChat) and Alibaba.

Short Bio: Tao Xie is a Chair Professor in the Department of Computer Science and Technology at Peking University, Beijing, China. He received an NSF CAREER Award, IEEE CS TCSE Distinguished Service Award, and various industrial faculty awards and distinguished/best paper awards. He is a co-Editor-in-Chief of the Wiley journal of Software Testing, Verification and Reliability (STVR). He served as the ISSTA 2015 Program Chair and the Tapia 2017/2018 Program/General Chair, and serves as an ICSE 2021 Program Co-Chair. He was selected by Lero as a David Lorge Parnas Fellow in 2019. He was selected as an ACM Distinguished Scientist in 2015, an IEEE Fellow in 2018, and an AAAS Fellow in 2019.

Tao Xie

Peking University, USA

China

Workshop

Mobile apps now have become the most popular way of accessing the Internet as well as performing daily tasks, e.g., reading, shopping, banking and chatting. Different from traditional desktop applications, mobile apps are typically developed under time-to-market pressure and facing severe competitions. Therefore, for app developers and companies, it is crucial to accelerate the app development process. Towards this target, we are leveraging the large-scale mobile apps as the big data and data-driven methods like deep learning, program analysis to automate some app development process. In this talk, I will introduce some of my latest research works in automated software engineering within the app development cycle including extracting storyboard from apps for requirement engineering, constructing UI design gallery, converting UI design into code automatically, mobile app GUI animation testing, and accessibility testing.

Chunyang Chen

Monash University, Australia

Australia

I am a lecturer (a.k.a Assistant Professor) at Monash University. My primary research interests are in Software Engineering, Applied Data Analysis, Deep Learning, and Human-Computer Interaction. I am especially concerned with applying data analysis to automated software development and mining software repositories.

Workshop

Conducting measurement-based experiments is fundamental for assessing the quality of Android apps in terms of, e.g., energy consumption, CPU, and memory usage. However, orchestrating such experiments is not trivial as it requires large boilerplate code, careful setup of measurement tools, and the adoption of various empirical best practices scattered across the literature. All together, those factors are slowing down the scientific advancement and harming experiments’ replicability in the mobile software engineering area. In this paper we present Android Runner (AR), a framework for automatically executing measurement-based experiments on native and web apps running on Android devices. In AR, an experiment is defined once in a descriptive fashion, and then its execution is fully automatic, customizable, and replicable. AR is implemented in Python and it can be extended with third-party profilers. AR has been used in more than 25 scientific studies primarily targeting performance and energy efficiency.

Ivano Malavolta

Vrije Universiteit Amsterdam

Netherlands

Eoin Grua

Vrije Universiteit Amsterdam

Cheng-Yu Lam

Randy de Vries

Franky Tan

Eric Zielinski

Michael Peters

Luuk Kaandorp

Workshop

Zhehao Fan

Tianjin University

China

Zhiyong Feng

Tianjin University

China

Xiao Xue

Shizhan Chen

Hongyue Wu

Workshop

Li Li

Monash University, Australia

Australia

Jun Gao

University of Luxembourg, Luxembourg

China

Pingfan Kong

Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg

Haoyu Wang

Beijing University of Posts and Telecommunications, China

China

Mengyu Huang

Monash University

Australia

Yuan-Fang Li

Monash University

Australia

Tegawendé F. Bissyandé

University of Luxembourg, Luxembourg

Luxembourg

Workshop

Huiyi Wang

Beijing University of Posts and Telecommunications

China

Liu Wang

Beijing University of Posts and Telecommunications

China

Haoyu Wang

Beijing University of Posts and Telecommunications, China

China

Workshop

Nowadays, energy efficiency is recognized as a core quality attribute of applications (apps) running on Android-powered devices constrained by their battery. Indeed, energy hogging apps are a liability to both the end-user and software developer. Yet, there are very few tools available to help developers increase the quality of their native code by ridding it of energy-related bugs. Android Studio is the official IDE for millions of developers worldwide and there’s no better place to enforce green coding rules in everyday projects. Indeed, Android Studio provides a code scanning tool called Android lint that can be extended with lacking green checks in order to foster the design of more eco-responsible apps.

Olivier Le Goaer

LIUPPA, Université de Pau et des Pays de l'Adour

France