Börje F. Karlsson

Caiipa: automated large-scale mobile app testing through contextual fuzzing

Scalable and comprehensive testing of mobile apps is extremely challenging. Every test input needs to be run with a variety of contexts, such as: device heterogeneity, wireless network speeds, locations, and unpredictable sensor inputs. The range of values for each context, e.g. location, can be very large. In this paper we present Caiipa, a cloud service for testing apps over an expanded mobile context space in a scalable way. It incorporates key techniques to make app testing more tractable, including a context test space prioritizer to quickly discover failure scenarios for each app. We have implemented Caiipa on a cluster of VMs and real devices that can each emulate various combinations of contexts for tablet and phone apps. We evaluate Caiipa by testing 265 commercially available mobile apps based on a comprehensive library of real-world conditions. Our results show that Caiipa leads to improvements of 11.1x and 8.4x in the number of crashes and performance bugs discovered compared to conventional UI-based automation (i.e., monkey-testing).

SIFT: building an internet of safe things

As the number of connected devices explodes, the use scenarios of these devices and data have multiplied. Many of these scenarios, e.g., home automation, require tools beyond data visualizations, to express user intents and to ensure interactions do not cause undesired effects in the physical world. We present SIFT, a safety-centric programming platform for connected devices in IoT environments. First, to simplify programming, users express high-level intents in declarative IoT apps. The system then decides which sensor data and operations should be combined to satisfy the user requirements. Second, to ensure safety and compliance, the system verifies whether conflicts or policy violations can occur within or between apps. Through an office deployment, user studies, and trace analysis using a large-scale dataset from a commercial IoT app authoring platform, we demonstrate the power of SIFT and highlight how it leads to more robust and reliable IoT apps.

Systematically Debugging IoT Control System Correctness for Building Automation

Advances and standards in Internet of Things (IoT) have simplified the realization of building automation. However, non-expert IoT users still lack tools that can help them to ensure the underlying control system correctness: user-programmable logics match the user intention. In fact, non-expert IoT users lack the necessary know-how of domain experts. This paper presents our experience in running a building automation service based on the Salus framework. Complementing efforts that simply verify the IoT control system correctness, Salus takes novel steps to tackle practical challenges in automated debugging of identified policy violations, for non-expert IoT users. First, Salus leverages formal methods to localize faulty user-programmable logics. Second, to debug these identified faults, Salus selectively transforms the control system logics into a set of parameterized equations, which can then be solved by popular model checking tools or SMT (Satisfiability Modulo Theories) solvers. Through office deployments, user studies, and public datasets, we demonstrate the usefulness of Salus in systematically debugging the correctness of IoT control systems for building automation.

How to the Smash Next Billion Mobile App Bugs

With users increasingly dependent on their phones, tablets, and wearables, the mobile app ecosystem is more important today than ever before. Creating and distributing apps has never been more accessible. Even single developers can now reach global audiences. But mobile apps must cope with extremely varied and dynamic operating conditions due to factors like diverse device characteristics, wireless network heterogeneity, and varied user behavior. App developers and operators of app marketplaces both lack testing tools that can effectively account for such diversity and, as a result, app failures and performance bugs (like excessive energy consumption) are commonly found today. To address this challenge to mobile app development, we have developed key techniques for scalable automated mobile app testing within two prototype services --- VanarSena and Caiipa. In this paper, we describe our vision for SMASH, a unified cloud-based mobile app testing service that combines the strengths of both previous systems to tackle the complexities presently faced by testers of mobile apps.

Conceptual Model and System for Genre-Focused Interactive Storytelling

This paper describes a conceptual model for the definition of a genre in the context of Interactive Storytelling and its implementation in LogTell-R, a system for the interactive creation of stories. This work builds on a previous system and experiments with plan recognition and discusses the foundations of our model to allow the creation of varied and coherent stories within a genre.

ContextPlayer: learning contextual music preferences for situational recommendations

Music listening is a very personal and situational behaviour, which suggests that contextual information could be used to greatly enhance music recommendation experience. However, making such use of mobile context, while learning user profiles, is a challenging problem. This case study presents a system for collecting context and usage data from mobile devices, but targeted at recommending music via unsupervised learning of user profiles and relevant situations. The developed data flow system supports both short enough response times and longer asynchronous reasoning on the collected data; furthermore, the mobile phone acts not only as sensor, but the mobile app is directly tied to the effectiveness of the music service user experience (UX). This work describes our system design and discusses issues related to the problem space and to usability tests on such systems, based on an international user trial.

Demo: Towards Flexible and Scalable Indoor Navigation

Bootstrapping efforts and scalability issues hinder large-scale deployment of indoor navigation systems. We present FollowUs, an easily-deployable (bootstrap-free) and scalable indoor navigation system. In addition to robust navigation through real-time trace-following, FollowUs integrates cloud services to process and combine traces at large scale. It can also leverage optional floor plans to further enhance navigation performance. We designed and implemented FollowUs, including a mobile app and cloud services on Azure, and validate its real-world usability.

Therenow: what is happening over there, right now?

Imagine you need to know what is happening at a certain location. How could you achieve that? A search engine might be a choice, but the information may be outdated since the page was crawled. Radio and TV news can broadcast in real-time, but they focus only on hot events. Social networks can also be real-time, but how to request what you want and quickly spread your question?