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About

I am a a Computer Science Postdoc working on reducing, analyzing, and optimizing the energy consumption levels for software, by using source code analysis and manipulation techniques. I was also awarded an FCT grant for my PhD research. I am one of the founding members of the Green Software for Space Control Mission (GreenSSCM) project, the Software Repositories for Green Computing FLAD/NSF project, and the Green Software Lab: Green Computing as an Engineering Discipline (GSL) project.

I concluded my PhD at the University of Minho, under the MAP-i doctoral programme with the thesis titled "Energyware Engineering: Techniques and Tools for Green Software Development" under the Green Software Lab (GSL) project . I received my MSc degree in Informatics Engineering in 2013, with my thesis "Querying for Model-Driven Spreadsheets" under the SpreadSheets as a Programming Paradigm (SSaaPP) project.

Currently, my research interests focus on green computing, human-computer interaction, and source code analysis and manipulation.

Interest
Topics
Details

Details

  • Name

    Rui Alexandre Pereira
  • Role

    Assistant Researcher
  • Since

    01st July 2013
  • Nationality

    Portugal
  • Contacts

    +351253604440
    rui.a.pereira@inesctec.pt
001
Publications

2020

SPELLing out energy leaks: Aiding developers locate energy inefficient code

Authors
Pereira, R; Carcao, T; Couto, M; Cunha, J; Fernandes, JP; Saraiva, J;

Publication
Journal of Systems and Software

Abstract
Although hardware is generally seen as the main culprit for a computer's energy usage, software too has a tremendous impact on the energy spent. Unfortunately, there is still not enough support for software developers so they can make their code more energy-aware. This paper 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 source code. The result of our technique is an energy ranking of source code fragments pointing developers to possible energy leaks in their code. This technique was implemented in the SPELL toolkit. Finally, in order to evaluate our technique, we conducted an empirical study where we asked participants to optimize the energy efficiency of a software system using our tool, while also having two other groups using no tool assistance and a profiler, respectively. We showed statistical evidence that developers using our technique were able to improve the energy efficiency by 43% on average, and even out performing a profiler for energy optimization. © 2019 Elsevier Inc.

2019

GreenHub Farmer: Real-world data for android energy mining

Authors
Matalonga, H; Cabral, B; Castor, F; Couto, M; Pereira, R; de Sousa, SM; Fernandes, JP;

Publication
IEEE International Working Conference on Mining Software Repositories

Abstract
As mobile devices are supporting more and more of our daily activities, it is vital to widen their battery up-time as much as possible. In fact, according to the Wall Street Journal, 9/10 users suffer from low battery anxiety. The goal of our work is to understand how Android usage, apps, operating systems, hardware and user habits influence battery lifespan. Our strategy is to collect anonymous raw data from devices all over the world, through a mobile app, build and analyze a large-scale dataset containing real-world, day-to-day data, representative of user practices. So far, the dataset we collected includes 12 million+ (anonymous) data samples, across 900+ device brands and 5.000+ models. And, it keeps growing. The data we collect, which is publicly available and by different channels, is sufficiently heterogeneous for supporting studies with a wide range of focuses and research goals, thus opening the opportunity to inform and reshape user habits, and even influence the development of both hardware and software for mobile devices. © 2019 IEEE.

2018

Energyware analysis

Authors
Pereira, R; Couto, M; Ribeiro, F; Rua, R; Saraiva, J;

Publication
CEUR Workshop Proceedings

Abstract
This documents introduces \Energyware" as a software engineering discipline aiming at defining, analyzing and optimizing the energy consumption by software systems. In this paper we present energyware analysis in the context of programming languages, software data structures and program's source code. For each of these areas we describe the research work done in the context of the Green Software Laboratory at Minho University: we describe energyaware techniques, tools, libraries, and repositories. © 2018 by the paper's authors.

2018

jStanley: placing a green thumb on Java collections

Authors
Pereira, R; Simão, P; Cunha, J; Saraiva, J;

Publication
Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering, ASE 2018, Montpellier, France, September 3-7, 2018

Abstract

2017

Products go Green: Worst-Case Energy Consumption in Software Product Lines

Authors
Couto, M; Borba, P; Cunha, J; Fernandes, JP; Pereira, R; Saraiva, J;

Publication
Proceedings of the 21st International Systems and Software Product Line Conference, SPLC 2017, Volume A, Sevilla, Spain, September 25-29, 2017

Abstract
The optimization of software to be (more) energy efficient is becoming a major concern for the software industry. Although several techniques have been presented to measure energy consumption for software, none has addressed software product lines (SPLs). Thus, to measure energy consumption of a SPL, the products must be generated and measured individually, which is too costly. In this paper, we present a technique and a prototype tool to statically estimate the worst case energy consumption for SPL. The goal is to provide developers with techniques and tools to reason about the energy consumption of all products in a SPL, without having to produce, run and measure the energy in all of them. Our technique combines static program analysis techniques and worst case execution time prediction with energy consumption analysis. This technique analyzes all products in a feature-sensitive manner, that is, a feature used in several products is analyzed only once, while the energy consumption is estimated once per product. We implemented our technique in a tool called Serapis. We did a preliminary evaluation using a product line for image processing implemented in C. Our experiments considered 7 products from such line and our initial results show that the tool was able to estimate the worst-case energy consumption with a mean error percentage of 9.4% and standard deviation of 6.2% when compared with the energy measured when running the products. © 2017 ACM.