Mobile app developers are able to design sophisticated user interfaces (UIs) that can improve a user's experience and contribute to an app's success. Developers invest in automated UI testing techniques, such as crawlers, to ensure that their app's UIs have a high level of quality. However, UI implementation mechanisms have changed significantly due to the availability of new APIs and mechanisms, such as fragments. In this paper, we study a large set of real-world apps to identify whether the mechanisms developers use to implement app UIs cause problems for those automated techniques. In addition, we examined the changes in these practices over time. Our results indicate that dynamic analyses face challenges in terms of completeness and changing development practices motivate the use of additional analyses, such as static analyses. We also discuss the implications of our results for current testing techniques and the design of new analyses.

Local databases have become an important component within mobile applications. Developers use local databases to provide mobile users with a responsive and secure service for data storage and access. However, using local databases comes with a cost. Studies have shown that they are one of the most energy consuming components on mobile devices and misuseof their APIs can lead to performance and security problems. In this paper, we report the results of a large scale empirical study on 1,000 top ranked apps from the Google Play app store. Our results present a detailed look into the practices, costs, and potential problems associated with local database usage in deployed apps. We distill our findings into actionable guidance for developers and motivate future areas of research related to techniques to support mobile app developers.

Summary The energy consumption of mobile apps has become an important consideration for developers as the underlying mobile devices are constrained by battery capacity. Display represents a significant portion of an app's energy consumption—up to 60% of an app's total energy consumption. However, developers lack techniques to identify the user interfaces in their apps for which energy needs to be improved. This paper presents a technique for detecting display energy hotspots—user interfaces of a mobile app whose energy consumption is greater than optimal. The technique leverages display power modeling and automated display transformation techniques to detect these hotspots and prioritize them for developers. The evaluation of the technique shows that it can predict display energy consumption to within 14% of the ground truth and accurately rank display energy hotspots. Furthermore, the approach found 398 display energy hotspots in a set of 962 popular Android apps, showing the pervasiveness of this problem. For these detected hotspots, the average power savings that could be realized through better user interface design was 30%. Taken together, these results indicate that the approach represents a potentially impactful technique for helping developers to detect energy related problems and reduce the energy consumption of their mobile apps.

Abstract Software piracy is an important concern for application developers. Such concerns are especially relevant in mobile application development, where piracy rates can be greater than 90%. The most common approach used by mobile developers to prevent piracy is code obfuscation. However, the decision to apply such transformations is currently made without regard to the impacts of obfuscations on another area of increasing concern for mobile application developers, energy usage. Because both software piracy and battery life are important concerns, mobile application developers must strike a balance between protecting their applications and preserving the battery lives of their users' devices. To help them make such choices, we conducted an empirical study of the effects of 18 code obfuscations on the amount of energy consumed by executing a total of 21 usage scenarios spread across 11 Android applications on four different mobile phone platforms. The results of the study indicate that, while obfuscations can have a statistically significant impact on energy usage and are more likely to increase energy usage than to decrease energy usage, the magnitudes of such impacts are unlikely to be meaningful to mobile application users. Copyright © 2016 John Wiley & Sons, Ltd.

Energy consumption of mobile apps has become an important consideration as the underlying devices are constrained by battery capacity. Display represents a significant portion of an app's energy consumption. However, developers lack techniques to identify the user interfaces in their apps for which energy needs to be improved. In this paper, we present a technique for detecting display energy hotspots - user interfaces of a mobile app whose energy consumption is greater than optimal. Our technique leverages display power modeling and automated display transformation techniques to detect these hotspots and prioritize them for developers. In an evaluation on a set of popular Android apps, our technique was very accurate in both predicting energy consumption and ranking the display energy hotspots. Our approach was also able to detect display energy hotspots in 398 Android market apps, showing its effectiveness and the pervasiveness of the problem. These results indicate that our approach represents a potentially useful technique for helping developers to detect energy related problems and reduce the energy consumption of their mobile apps.

String analysis is critical for many verification techniques. However, accurately modeling string variables is a challeng- ing problem. Current approaches are generally customized for certain problem domains or have critical limitations in handling loops, providing context-sensitive inter-procedural analysis, and performing efficient analysis on complicated apps. To address these limitations, we propose a general framework, Violist, for string analysis that allows researchers to more flexibly choose how they will address each of these challenges by separating the representation and interpreta- tion of string operations. In our evaluation, we show that our approach can achieve high accuracy on both Java and Android apps in a reasonable amount of time. We also com- pared our approach with a popular and widely used string analyzer and found that our approach has higher precision and shorter execution time while maintaining the same level of recall.