Coffee Session: 13h30 -14h, Sala de Estar, 4th Floor.
Talk Session: 14h -14h30, Auditório A1, ground floor.
Leafactor: Improving Energy Efficiency of Android Apps via Automatic Refactoring – Luis Cru, Rui Abreu and Jean-Nöel Rouvignac
Leafactor is a tool to automatically improve the energy consumption of Android apps. It does so by refactoring the source code to follow a set of patterns known to be energy efficient. The toolset was validated using 222 refactorings in 140 open-source apps. Changes were submitted to the original apps by creating pull requests to the official projects.
Performance-based Guidelines for Energy Efficient Mobile Applications – Luís Cruz and Rui Abreu
Mobile and wearable devices are nowadays the de facto personal computers, while desktop computers are becoming less popular. Therefore, it is important for companies to deliver efficient mobile applications. As an example, Google has published a set of best practices to optimize the performance of Android applications. However, these guidelines fall short to address energy consumption. As mobile software applications operate in resource-constrained environments, guidelines to build energy efficient applications are of utmost importance. In this paper, we studied whether or not eight best performance-based practices have an impact on the energy consumed by Android applications. In an experimental study with six popular mobile applications, we observed that the battery of the mobile device can last up to approximately an extra hour if the applications are developed with energy-aware practices. This work paves the way for a set of guidelines for energy-aware automatic refactoring techniques.
Invited paper for the Poster Track at ICSE 2017.
Helping Programmers Improve the Energy Efficiency of Source Code – Rui Pereira, Tiago Carção, Marco Couto, Jácome Cunha, João Paulo Fernandes, João Saraiva
This paper briefly proposes a technique to detect energy inefficient fragments in the source code of a software system. Test cases are executed to obtain energy consumption measurements, and a statistical method, based on spectrum-based fault localization, is introduced to relate energy consumption to the system’s source code. The result of our technique is an energy ranking of source code fragments pointing developers to possible energy leaks in their code.
Green Software Lab members awarded new project funded by FLAD/NSF entitled “Quantitative Program Analysis”. This project is in collaboration with the University of Nebraska, Department of Computer Science with Prof. Matthew B. Dwyer.
The project aims to apply quantitative program analysis to analyze and optimize the energy consumption of software systems.
The Influence of the Java Collection Framework on Overall Energy Consumption – Rui Pereira, Marco Couto, Jácome Cunha, João Paulo Fernandes and João Saraiva
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.
In 5th International Workshop on Green and Sustainable Software (GREENS) [PDF] [Bib]
Haskell in Green Land: Analyzing the Energy Behavior of a Purely Functional Language – Luís Gabriel Lima, Gilberto Melfe, Paulo Lieuthier, Francisco Soares-Neto, Fernando Castor and João Paulo Fernandes
Recent work has studied the effect that factors such as code obfuscation, refactorings and data types have on energy efficiency. In this paper, we attempt to shed light on the energy behavior of programs written in a lazy purely functional language, Haskell. We have conducted two empirical studies to analyze the energy efficiency of Haskell programs from two different perspectives: strictness and concurrency. Our experimental space exploration comprises more than 2000 configurations and 20000 executions.
We found out that small changes can make a big difference in terms of energy consumption. For example, in one of our benchmarks, under a specific configuration, choosing one data sharing primitive (MVar) over another (TMVar) can yield 60% energy savings. In another benchmark, the latter primitive can yield up to 30% energy savings over the former. Thus, tools that support developers in quickly refactoring a program to switch between different primitives can be of great help if energy is a concern. In addition, the relationship between energy consumption and performance is not always clear. In sequential benchmarks, high performance is an accurate proxy for low energy consumption. However, for one of our concurrent benchmarks, the variants with the best performance also exhibited the worst energy consumption. To support developers in better understanding this complex relationship, we have extended two existing performance analysis tools to also collect and present data about energy consumption.
Green Software Lab members awarded new project funded by FCT/Slovakia entitled “Towards a Software Engineering Discipline for Green Software” (ref.: 441). This project is in collaboration with the Research and Investigation Agency of Slovakia, with prof. Csaba Szabó.
Green Software Lab members awarded new project funded by FLAD/NSF entitled “Software Repositories for Green Computing”. This project is in collaboration with the University of California, Irvine, Department of Informatics with Prof. Cristina Videira Lopes..
GreenDroid: A Tool for Analysing Energy Consumption in the Android Ecosystem – Marco Couto, Jácome Cunha, João Paulo Fernandes, Rui Pereira, João Saraiva
This paper presents GreenDroid, a tool for monitoring and analyzing power consumption for the Android ecosystem. This tool instruments the source code of a giving Android application and is able to estimate the power consumed when running it. Moreover, it uses advanced classification algorithms to detect abnormal power consumption and to relate them to fragments in the source code. A set of graphical results are produced that help software developers to identify abnormal power consumption in their source code.