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

Automating Software Development for Mobile Computing Platforms

Mobile devices such as smartphones and tablets have become ubiquitous in today\u27s computing landscape. These devices have ushered in entirely new populations of users, and mobile operating systems are now outpacing more traditional desktop systems in terms of market share. The applications that run on these mobile devices (often referred to as apps ) have become a primary means of computing for millions of users and, as such, have garnered immense developer interest. These apps allow for unique, personal software experiences through touch-based UIs and a complex assortment of sensors. However, designing and implementing high quality mobile apps can be a difficult process. This is primarily due to challenges unique to mobile development including change-prone APIs and platform fragmentation, just to name a few. in this dissertation we develop techniques that aid developers in overcoming these challenges by automating and improving current software design and testing practices for mobile apps. More specifically, we first introduce a technique, called Gvt, that improves the quality of graphical user interfaces (GUIs) for mobile apps by automatically detecting instances where a GUI was not implemented to its intended specifications. Gvt does this by constructing hierarchal models of mobile GUIs from metadata associated with both graphical mock-ups (i.e., created by designers using photo-editing software) and running instances of the GUI from the corresponding implementation. Second, we develop an approach that completely automates prototyping of GUIs for mobile apps. This approach, called ReDraw, is able to transform an image of a mobile app GUI into runnable code by detecting discrete GUI-components using computer vision techniques, classifying these components into proper functional categories (e.g., button, dropdown menu) using a Convolutional Neural Network (CNN), and assembling these components into realistic code. Finally, we design a novel approach for automated testing of mobile apps, called CrashScope, that explores a given android app using systematic input generation with the intrinsic goal of triggering crashes. The GUI-based input generation engine is driven by a combination of static and dynamic analyses that create a model of an app\u27s GUI and targets common, empirically derived root causes of crashes in android apps. We illustrate that the techniques presented in this dissertation represent significant advancements in mobile development processes through a series of empirical investigations, user studies, and industrial case studies that demonstrate the effectiveness of these approaches and the benefit they provide developers

Visual Recognition Of Graphical User Interface Components Using Deep Learning Technique

Graphical User Interface (GUI) building in software development is a process which ideally need to go through several steps. Those steps in the process start from idea or rough sketch of the GUI, then refined into visual design, implemented in coding or prototype, and finally evaluated for its function and usability to discover design problem and to get feedback from users. Those steps repeated until the GUI considered satisfactory or acceptable by the user. Computer vision technique has been researched and developed to make the process faster and easier; for example generating code for implementation, or automatic GUI testing using component images. But among those techniques, there are still few for usability testing purpose. This preliminary research attempted to make the foundation for usability testing using computer vision technique by built minimalist dataset which has images of various GUI components and used the dataset in deep learning experiment for GUI components visual recognition. The experiment results showed deep learning technique suitable for the intended task, with accuracy of 95% for recognition of two different types of components, and accuracy of 72% for six different types of component

A framework for the design, prototyping and evaluation of mobile interfaces for domestic environments

The idea of the smart home has been discussed for over three decades, but it has yet to achieve mass-market adoption. This thesis asks the question Why is my home not smart? It highlights four main areas that are barriers to adoption, and concentrates on a single one of these issues: usability. It presents an investigation that focuses on design, prototyping and evaluation of mobile interfaces for domestic environments resulting in the development of a novel framework. A smart home is the physical realisation of a ubiquitous computing system for domestic living. The research area offers numerous benefits to end-users such as convenience, assistive living, energy saving and improved security and safety. However, these benefits have yet to become accessible due to a lack of usable smart home control interfaces. This issue is considered a key reason for lack of adoption and is the focus for this thesis. Within this thesis, a framework is introduced as a novel approach for the design, prototyping and evaluation of mobile interfaces for domestic environments. Included within this framework are three components. Firstly, the Reconfigurable Multimedia Environment (RME), a physical evaluation and observation space for conducting user centred research. Secondly, Simulated Interactive Devices (SID), a video-based development and control tool for simulating interactive devices commonly found within a smart home. Thirdly, iProto, a tool that facilitates the production and rapid deployment of high fidelity prototypes for mobile touch screen devices. This framework is evaluated as a round-tripping toolchain for prototyping smart home control and found to be an efficient process for facilitating the design and evaluation of such interfaces

Software Usability

This volume delivers a collection of high-quality contributions to help broaden developers’ and non-developers’ minds alike when it comes to considering software usability. It presents novel research and experiences and disseminates new ideas accessible to people who might not be software makers but who are undoubtedly software users

Designing and evaluating the usability of a machine learning API for rapid prototyping music technology

To better support creative software developers and music technologists' needs, and to empower them as machine learning users and innovators, the usability of and developer experience with machine learning tools must be considered and better understood. We review background research on the design and evaluation of application programming interfaces (APIs), with a focus on the domain of machine learning for music technology software development. We present the design rationale for the RAPID-MIX API, an easy-to-use API for rapid prototyping with interactive machine learning, and a usability evaluation study with software developers of music technology. A cognitive dimensions questionnaire was designed and delivered to a group of 12 participants who used the RAPID-MIX API in their software projects, including people who developed systems for personal use and professionals developing software products for music and creative technology companies. The results from the questionnaire indicate that participants found the RAPID-MIX API a machine learning API which is easy to learn and use, fun, and good for rapid prototyping with interactive machine learning. Based on these findings, we present an analysis and characterization of the RAPID-MIX API based on the cognitive dimensions framework, and discuss its design trade-offs and usability issues. We use these insights and our design experience to provide design recommendations for ML APIs for rapid prototyping of music technology. We conclude with a summary of the main insights, a discussion of the merits and challenges of the application of the CDs framework to the evaluation of machine learning APIs, and directions to future work which our research deems valuable