ANDROID

2015

The use of powerful mobile devices, like smartphones, tablets and laptops, is changing the way programmers develop software. While in the past the primary goal to optimize software was the run time optimization, nowadays there is a growing awareness of the need to reduce energy consumption. This paper presents techniques and tools to detect anomalous energy consumption in Android applications, and to relate it directly with the source code of the application. We present a methodology to classify program execution and program methods according to the energy consumed. Thus, we monitored and analyzed the results of energy consumed by a corpus of Android applications, and we defined greenaware energy thresholds. Such thresholds are used to classify program methods according to energy consumption. Moreover, we manually inspect the source code of both energy efficient and inefficient methods and we identify program features that influence energy consumption.

Lecture Notes in Computer Science, 2014

2015 IEEE 13th International Scientific Conference on Informatics, 2015

2017 IEEE/ACM 39th International Conference on Software Engineering Companion (ICSE-C), 2017

2017 IEEE 24th International Conference on Software Analysis, Evolution and Reengineering (SANER), 2017

Cellular phones and tablets are ubiquitous, with a market penetration that is counted in millions of active users and units sold. The increasing computing capabilities and strict autonomy requirements on mobile devices drive a particular concern on energy utilization and optimization of this kind of equipment. In this paper, we investigate an approach to relate the energy consumption of smartphones with the operational status of the device, surveying parameters exposed by the operating system using an Android application. Our goal is to explore the means to expand the information that may help to produce more reliable measurements that can be used in further research for designing energy optimization profiles for mobile devices and identify optimization needs.

Measuring the energy spent by a software application is a problem that can be solved by having the proper hardware or software instruments. However, not always such tools are available or the provide resolution cannot fit the needs of the user, for instance when measuring a very small piece of code. This problem is particularly relevant on mobile software products, as they are developed to be executed in an environment limited in energy resources. Mobile software engineers should take special consideration on the energy consumption when designing and implementing an application. In this paper, we propose that the energy consumed by a unit of code can be approximated by the execution time. Using software benchmarks run with different data loads, we measured the execution time required to complete the job, and using a software tool to measure the energy spent during the execution of the benchmark, with the objective of finding a relationship among them. We observed that, regardless of the software benchmark, the data load injected and the programming language, of implementation the ratio between the execution time and the energy consumption remains consistent, opening the opportunity to develop techniques to approximate the energy consumption of mobile software based on execution time measurements.

2015 IEEE 35th International Conference on Distributed Computing Systems, 2015

ArXiv, 2016

The boom in mobile apps has changed the traditional landscape of software development by introducing new challenges due to the limited resources of mobile devices, e.g., memory, CPU, network bandwidth and battery. The energy consumption of mobile apps is nowadays a hot topic and researchers are actively investigating the role of coding practices on energy efficiency. Recent studies suggest that design quality can conflict with energy efficiency. Therefore, it is important to take into account energy efficiency when evolving the design of a mobile app. The research community has proposed approaches to detect and remove anti-patterns (i.e., poor solutions to design and implementation problems) in software systems but, to the best of our knowledge, none of these approaches have included anti-patterns that are specific to mobile apps and--or considered the energy efficiency of apps. In this paper, we fill this gap in the literature by analyzing the impact of eight type of anti-patterns ...

The hype in the popularity of recent wireless technologies has increased applications of smartphones in various fields, particularly, education and health care. The trend of increasing application functionality to enrich smartphone users experience requires detailed insights of application energy consumption behavior. Smartphone application energy estimation helps investigate energy consumption behavior of applications at diversified granularity when it is run on resource-constrained devices. Fine granular estimation gives more insights to the application energy consumption behavior to assist developers to propose resource-friendly application designs. This study proposes a lightweight code analysis–based estimation framework to minimize high profiling overhead of use-based estimation methods. Moreover, it analyzes estimation overhead and accuracy of existing dynamic estimation tools to present a case for code analysis–based energy estimation method. The estimated energy is found 86% accurate to the ground truth value for a set of benchmarks using our proposed framework.