Overcoming Language Dichotomies: Toward Effective Program Comprehension for Mobile App Development

Mobile devices and platforms have become an established target for modern software developers due to performant hardware and a large and growing user base numbering in the billions. Despite their popularity, the software development process for mobile apps comes with a set of unique, domain-specific challenges rooted in program comprehension. Many of these challenges stem from developer difficulties in reasoning about different representations of a program, a phenomenon we define as a "language dichotomy". In this paper, we reflect upon the various language dichotomies that contribute to open problems in program comprehension and development for mobile apps. Furthermore, to help guide the research community towards effective solutions for these problems, we provide a roadmap of directions for future work.Comment: Invited Keynote Paper for the 26th IEEE/ACM International Conference on Program Comprehension (ICPC'18

Supporting Evolution and Maintenance of android Apps

Mobile developers and testers face a number of emerging challenges. These include rapid platform evolution and API instability; issues in bug reporting and reproduction involving complex multitouch gestures; platform fragmentation; the impact of reviews and ratings on the success of their apps; management of crowd-sourced requirements; continuous pressure from the market for frequent releases; lack of effective and usable testing tools; and limited computational resources for handheld devices. Traditional and contemporary methods in software evolution and maintenance were not designed for these types of challenges; therefore, a set of studies and a new toolbox of techniques for mobile development are required to analyze current challenges and propose new solutions. This dissertation presents a set of empirical studies, as well as solutions for some of the key challenges when evolving and maintaining android apps. In particular, we analyzed key challenges experienced by practitioners and open issues in the mobile development community such as (i) android API instability, (ii) performance optimizations, (iii) automatic GUI testing, and (iv) energy consumption. When carrying out the studies, we relied on qualitative and quantitative analyses to understand the phenomena on a large scale by considering evidence extracted from software repositories and the opinions of open-source mobile developers. From the empirical studies, we identified that dynamic analysis is a relevant method for several evolution and maintenance tasks, in particular, because of the need of practitioners to execute/validate the apps on a diverse set of platforms (i.e., device and OS) and under pressure for continuous delivery. Therefore, we designed and implemented an extensible infrastructure that enables large-scale automatic execution of android apps to support different evolution and maintenance tasks (e.g., testing and energy optimization). In addition to the infrastructure we present a taxonomy of issues, single solutions to the issues, and guidelines to enable large execution of android apps. Finally, we devised novel approaches aimed at supporting testing and energy optimization of mobile apps (two key challenges in evolution and maintenance of android apps). First, we propose a novel hybrid approach for automatic GUI-based testing of apps that is able to generate (un)natural test sequences by mining real applications usages and learning statistical models that represent the GUI interactions. In addition, we propose a multi-objective approach for optimizing the energy consumption of GUIs in android apps that is able to generate visually appealing color compositions, while reducing the energy consumption and keeping a design concept close to the original

A Survey of Performance Optimization for Mobile Applications

Nowadays there is a mobile application for almost everything a user may think of, ranging from paying bills and gathering information to playing games and watching movies. In order to ensure user satisfaction and success of applications, it is important to provide high performant applications. This is particularly important for resource constraint systems such as mobile devices. Thereby, non-functional performance characteristics, such as energy and memory consumption, play an important role for user satisfaction. This paper provides a comprehensive survey of non-functional performance optimization for Android applications. We collected 155 unique publications, published between 2008 and 2020, that focus on the optimization of non-functional performance of mobile applications. We target our search at four performance characteristics, in particular: responsiveness, launch time, memory and energy consumption. For each performance characteristic, we categorize optimization approaches based on the method used in the corresponding publications. Furthermore, we identify research gaps in the literature for future work