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Energy Efficiency across Programming Languages: How Do Energy, Time, and Memory Relate?, Pereira, Rui, Couto Marco, Ribeiro Francisco, Rua Rui, Cunha Jácome, Fernandes João P., and Saraiva João , 10th ACM SIGPLAN International Conference on Software Language Engineering (SLE’17), 23-24 October, Vancouver, Canada, (2017) paper.pdf
User-Friendly Spreadsheet Querying: An Empirical Study, Pereira, Rui, Saraiva João, Cunha Jácome, and Fernandes João P. , 31st Annual ACM Symposium on Applied Computing (SAC'16), Smart Human Computer Interaction Track, Poster Paper, Pisa, Italy, (2016) sac-hci16.pdf
The Influence of the Java Collection Framework on Overall Energy Consumption, Pereira, Rui, Couto Marco, Saraiva João, Cunha Jácome, and Fernandes João P. , 5th International Workshop on Green and Sustainable Software (ICSE 2016), 15-21, p.–, (2016) Abstractgreens.pdf

This paper presents a detailed study of the energy consumption of the different Java Collection Framework (JFC) implementations. For each method of an implementation in this framework, we present its energy consumption when handling different amounts of data. Knowing the greenest methods for each implementation, we present an energy optimization approach for Java programs: based on calls to JFC methods in the source code of a program, we select the greenest implementation. Finally, we present preliminary results of optimizing a set of Java programs where we obtained 6.2% energy savings.

Helping Programmers Improve the Energy Efficiency of Source Code (Abstract/Poster), Pereira, Rui, Carção Tiago, Couto Marco, Cunha Jácome, Fernandes João P., and Saraiva João , Proceedings of the 39th International Conference on Software Engineering (ICSE 2017), Buenos Aires, Argentina, (2017) paper.pdfpostera3.pdf
Memoization for Saving Energy in Android Applications: When and how to di it, Pinto, Adriano, Couto Marco, and Cunha Jácome , (Submitted) Abstractpaper.pdf

Over the last few years, the interest in the analysis of the energy consumption of Android applications has been increasing significantly. Indeed, there are a considerable number of studies which aim at analyzing the energy consumption in various ways, such as measuring/estimating the energy consumed by an application or block of code, or even detecting energy expensive coding patterns or API's.

Nevertheless, when it comes to actually improving the energy efficiency of an application, we face a whole new challenge, which can only be achieved through source code improvements that can take advantage of energy saving techniques. However, there is still a lack of information about such techniques and their impact on energy consumption.

In this paper, we analyze the impact of the memoization technique in the energy consumption of Android applications. We present a systematic study of the use of memoization, where we compare implementations of 18 method from different applications, with and without using memoization, and measure the energy consumption of both of them. Using this approach, we are able to characterize Android methods that should be memoized.

Our results show that using memoization can clearly be a good approach for saving energy. For the 18 tested methods, 13 of them decreased significantly their energy consumption, while for the remaining 5 we observed unpredictable behavior in 3 of them and an overall increase of energy consumption in the last 2. We also included a discussion about when is actually beneficial to use memoization for saving energy, and what is the expected percentage of gain/loss when memoization works and when it does not.