Reviewing the Efficiency of Current Power-Saving Approaches Used Among Different Stages of an Android-Application Lifecycle

info:eu-repo/semantics/publishedVersio

Perceived-Color Approximation Transforms for Programs that Draw

© 1981-2012 IEEE. Human color perception acuity is not uniform across colors. This makes it possible to transform drawing programs to generate outputs whose colors are perceptually equivalent but numerically distinct. One benet of such transformations is lower display power dissipation on organic light-emitting diode (OLED) displays. We introduce Ishihara, a language for 2D drawing that lets programs specify perceptual-color equivalence classes to use in drawing operations enabling compile-time and runtime transformations that trade perceived color accuracy for lower OLED display power dissipation

Kansei Engineering Approach for Designing a Self-monitoring Blood Glucose Application

The proportion of people with diabetes in Indonesia is 6.9%, of whom, 90% suffer from type 2 diabetes. To control diabetes, the International Diabetes Foundation has a self-monitoring blood glucose program (SMBG) that is designed to gather detailed information about blood glucose, which can be used to define the required dietary and physical activity. In Indonesia, SMBGs have not been applied widely due to some difficulties with the SMBG application, including difficulty with interpreting blood glucose levels and the required food consumption. The rapid development of smartphones and the internet in recent years may provide a solution for SMBG applications in Indonesia. Therefore, the aim of this research is to design an SMBG application that meets the needs of people with diabetes. In designing the application, the Kansei Engineering method was used due to its ability to capture impressions of the product that reflect hidden needs. Kansei Engineering was applied to translate the patient’s perception into design elements. Statistical analysis – particularly Factor Analysis and Partial Least Squares Analysis – were used to support the Kansei Engineering method. Statistical analysis indicated that four main design components needed to be considered in the design, including ‘data record persistence’, ‘ease of use’, ‘data presentation’, and ‘visual attractiveness’. These four components were used as the basic design concepts, which were transferred to new design specifications based on Partial Least Squares Analysis. The selected design elements (or premium Kansei) were ‘color scheme – light’, ‘language – Bahasa’, and ‘input dialog – typing’. The result of this research is a design for an SMBG android-based application that is easy to use and also provides appropriate information to patients with diabetes so they can plan their diet program

An Empirical Study on the Impact of Deep Parameters on Mobile App Energy Usage

Improving software performance through configuration parameter tuning is a common activity during software maintenance. Beyond traditional performance metrics like latency, mobile app developers are interested in reducing app energy usage. Some mobile apps have centralized locations for parameter tuning, similar to databases and operating systems, but it is common for mobile apps to have hundreds of parameters scattered around the source code. The correlation between these deep parameters and app energy usage is unclear. Researchers have studied the energy effects of deep parameters in specific modules, but we lack a systematic understanding of the energy impact of mobile deep parameters. In this paper we empirically investigate this topic, combining a developer survey with systematic energy measurements. Our motivational survey of 25 Android developers suggests that developers do not understand, and largely ignore, the energy impact of deep parameters. To assess the potential implications of this practice, we propose a deep parameter energy profiling framework that can analyze the energy impact of deep parameters in an app. Our framework identifies deep parameters, mutates them based on our parameter value selection scheme, and performs reliable energy impact analysis. Applying the framework to 16 popular Android apps, we discovered that deep parameter-induced energy inefficiency is rare. We found only 2 out of 1644 deep parameters for which a different value would significantly improve its app\u27s energy efficiency. A detailed analysis found that most deep parameters have either no energy impact, limited energy impact, or an energy impact only under extreme values. Our study suggests that it is generally safe for developers to ignore the energy impact when choosing deep parameter values in mobile apps

Adding Energy Star Rating Schema to Android Applications on Google Play Store an Example of a Preventive Power Saving Model

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Genetic Improvement of Software: a Comprehensive Survey

Genetic improvement (GI) uses automated search to find improved versions of existing software. We present a comprehensive survey of this nascent field of research with a focus on the core papers in the area published between 1995 and 2015. We identified core publications including empirical studies, 96% of which use evolutionary algorithms (genetic programming in particular). Although we can trace the foundations of GI back to the origins of computer science itself, our analysis reveals a significant upsurge in activity since 2012. GI has resulted in dramatic performance improvements for a diverse set of properties such as execution time, energy and memory consumption, as well as results for fixing and extending existing system functionality. Moreover, we present examples of research work that lies on the boundary between GI and other areas, such as program transformation, approximate computing, and software repair, with the intention of encouraging further exchange of ideas between researchers in these fields

Achievements, Open Problems and Challenges for Search Based Software Testing

testing as an optimisation problem, which can be attacked using computational search techniques from the field of Search Based Software Engineering (SBSE). We present an analysis of the SBST research agenda1, focusing on the open problems and chal-lenges of testing non-functional properties, in particular a topic we call ‘Search Based Energy Testing ’ (SBET), Multi-objective SBST and SBST for Test Strategy Identification. We conclude with a vision of FIFIVERIFY tools, which would automatically find faults, fix them and verify the fixes. We explain why we think such FIFIVERIFY tools constitute an exciting challenge for the SBSE community that already could be within its reach. I