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

Resource consumption analysis of online activity recognition on mobile phones and smartwatches

Most of the studies on human activity recognition using smartphones and smartwatches are performed in an offline manner. In such studies, collected data is analyzed in machine learning tools with less focus on the resource consumption of these devices for running an activity recognition system. In this paper, we analyze the resource consumption of human activity recognition on both smartphones and smartwatches, considering six different classifiers, three different sensors, different sampling rates and window sizes. We study the CPU, memory and battery usage with different parameters, where the smartphone is used to recognize seven physical activities and the smartwatch is used to recognize smoking activity. As a result of this analysis, we report that classification function takes a very small amount of CPU time out of total app’s CPU time while sensing and feature calculation consume most of it. When an additional sensor is used besides an accelerometer, such as gyroscope, CPU usage increases significantly. Analysis results also show that increasing the window size reduces the resource consumption more than reducing the sampling rate. As a final remark, we observe that a more complex model using only the accelerometer is a better option than using a simple model with both accelerometer and gyroscope when resource usage is to be reduced

SPS: an SMS-based Push Service for Energy Saving in Smartphone\u27s Idle State

Despite of all the advances in smartphone technology in recent years, smartphones still remain limited by their battery life. Unlike other power hungry components in the smartphone, the cellular data and Wi-Fi interfaces often continue to be used even while the phone is in the idle state to accommodate unnecessary data traffic produced by some applications. In addition, bad reception has been proven to greatly increase energy consumed by the radio, which happens quite often when smartphone users are inside buildings. In this paper, we present a Short message service Push based Service (SPS) to save unnecessary power consumption when smartphones are in idle state, especially in bad reception areas. First, SPS disables a smartphone\u27s data interfaces whenever the phone is in idle state. Second, to preserve the real-time notification functionality required by some apps, such as new email arrivals and social media updates, when a notification is needed, a wakeup text message will be received by the phone, and then SPS enables the phone\u27s data interfaces to connect to the corresponding server to retrieve notification data via the normal data network. Once the notification data has been retrieved, SPS will disable the data interfaces again if the phone is still in idle state. We have developed a complete prototype for Android smartphones. Our experiments show that SPS consumes less energy than the current approach. In areas with bad reception, the SPS prototype can double the battery life of a smartphone