With the widespread use and adoption of mobile platforms like Android a new software quality concern has emerged - energy consumption. However, developing energy-efficient software and applications requires knowledge and like-wise proper tooling to support mobile developers. To this aim, we present an approach to examine the energy evolution of software revisions based on their API interactions. The approach stems from the assumption that the utilization of an API has direct implications on the energy being consumed during runtime. Based on an empirical evaluation, we show initial results that API interactions serve as a flexible, lightweight, and effective way to compare software revisions regarding their energy evolution. Given our initial results we envision that in future using our approach mobile developers will be able to gain insights on the energy implications of changes in source code in the course of the software development life-cycle.

Background: Third-party software libraries often serve as fundamental building blocks for developing applications. However, depending on such libraries for development raises a new concern, energy consumption, which has become of increased interest for the software engineering community in recent years. Understanding the energy implications of software design choices is an ongoing research challenge, especially when working with mobile devices that are constrained by limited battery life. Aims: Our goal is to research approaches, which will support software developers to better comprehend the energy implications of software design choices on Android. For this study, we particularly focus on APIs from third-party libraries for which we research methods that will enable estimating the energy consumption without the need for specific measurement hardware and laborious measurements. Method: To achieve the stipulated goal we introduce System API Utilization Profiles (uAPI) which are based on the general assumption that the actual energy consumption of a library is directly tied to its utilization of the underlying System API. We provide a formal definition and implementation of the proposed uAPI profiles that are calculated based on dynamic call graphs obtained from a library under test. To further show the connection between our proposed uAPI profiles and energy consumption, we empirically examined their correlation using two experiments. The first one is dedicated to Android I/O operations, the second one examines uAPI profiles based on a popular open-source library for JSON document processing. Results: In our empirical evaluation, we collected 1052 individual call graphs which we used as an input to evaluate the our model and compare it with the actual energy consumption measured. For each call graph, we measured the energy consumption with special hardware and attributed the measurements to individual methods. Based on that data, we examined a strong linear correlation between the proposed uAPI profiles and the actual measured energy consumption. Conclusions: uApi profiles serve as a lightweight and feasible approach to characterize the energy characteristics of third-party libraries. Their computation does not involve any special hardware, and there are no alterations on the target mobile device required. For future work, we investigate possibilities to use the proposed uAPI profiles as a foundation for a regression model, which would allow us to predict the energy consumption development of a library over time.

With the advent of mobile application development a new software quality concern - energy consumption - was introduced. For mobile software developers knowledge about software and algorithm design choices and their implications on energy consumption is crucial. However, software developers either lack this knowledge or tools to support them in estimating the energy consumption of their applications and therefore are unable to reflect on their design choices. In this empirical study we examine the energy consumption of 12 sorting algorithms and the resulting energy impact when used with different data types. We propose a methodology to obtain energy readings and relate them to application execution traces. Our results show that the choice of data type together with algorithm design can have significant impact on the energy profile of an application.

The demand for high quality mobile applications is constantly rising, especially in mission critical settings. Thus, new software engineering methodologies are needed in order to ensure the desired quality of an application. The research presented proposes a quality assurance methodology for mobile applications through test automation by optimizing test suites. The desired goal is to find a minimal test suite while maintaining efficiency and reducing execution cost. Furthermore to avoid invalidating an optimized test suite as the system under test evolves, the approach further proposes to extract patterns from the applied changes to an application. The evaluation plan comprises a combination of an empirical and an industrial case study based on open source projects and an industrial project in the healthcare domain. It is expected that the presented approach supports the testing process on mobile application platforms.

DECT[3] is a widely deployed standard mostly used for short range wireless phones.So far, no method has been published which is able to recover the audio signal in a call that is encrypted and lasts only for a few minutes.In this paper, we present a method that recovers the audio signal sent from the phone to its base station in an encrypted call.To do so, we use a replay-attack against the phone to recover the key streams, which were used to encrypt the call.The method is applicable to short calls too, where not enough keystreams are available to recover the ciphers key using a key recovery attack[8] on DSC.The method is fast and practical and can be executed at very low cost.