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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.

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Details

Details

  • Name

    Rui Alexandre Pereira
  • Cluster

    Computer Science
  • Role

    Assistant Researcher
  • Since

    01st July 2013
003
Publications

2021

Ranking programming languages by energy efficiency

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

Publication
Science of Computer Programming

Abstract
This paper compares a large set of programming languages regarding their efficiency, including from an energetic point-of-view. Indeed, we seek to establish and analyze different rankings for programming languages based on their energy efficiency. The goal of being able to rank programming languages based on their energy efficiency is both recent, and certainly deserves further studies. We have taken rigorous and strict solutions to 10 well defined programming problems, expressed in (up to) 27 programming languages, from the well known Computer Language Benchmark Game repository. This repository aims to compare programming languages based on a strict set of implementation rules and configurations for each benchmarking problem. We have also built a framework to automatically, and systematically, run, measure and compare the energy, time, and memory efficiency of such solutions. Ultimately, it is based on such comparisons that we propose a series of efficiency rankings, based on single and multiple criteria. Our results show interesting findings, such as how slower/faster languages can consume less/more energy, and how memory usage influences energy consumption. We also present a simple way to use our results to provide software engineers and practitioners support in deciding which language to use when energy efficiency is a concern. In addition, we further validate our results and rankings against implementations from a chrestomathy program repository, Rosetta Code., by reproducing our methodology and benchmarking system. This allows us to understand how the results and conclusions from our rigorously and well defined benchmarked programs compare to those based on more representative and real-world implementations. Indeed our results show that the rankings do not change apart from one programming language. © 2021 Elsevier B.V.

2021

Bringing Green Software to Computer Science Curriculum: Perspectives from Researchers and Educators

Authors
Saraiva, J; Zong, Z; Pereira, R;

Publication
ITiCSE 2021: 26th ACM Conference on Innovation and Technology in Computer Science Education, Virtual Event, Germany, June 26 - July 1, 2021.

Abstract

2021

GreenHub: a large-scale collaborative dataset to battery consumption analysis of android devices

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

Publication
EMPIRICAL SOFTWARE ENGINEERING

Abstract
Context The development of solutions to improve battery life in Android smartphones and the energy efficiency of apps running on them is hindered by diversity. There are more than 24k Android smartphone models in the world. Moreover, there are multiple active operating system versions, and a myriad application usage profiles. Objective In such a high-diversity scenario, profiling for energy has only limited applicability. One would need to obtain information about energy use in real usage scenarios to make informed, effective decisions about energy optimization. The goal of our work is to understand how Android usage, apps, operating systems, hardware, and user habits influence battery lifespan. Method We leverage crowdsourcing to collect information about energy in real-world usage scenarios. This data is collected by a mobile app, which we developed and made available to the public through Google Play store, and periodically uploaded to a centralized server and made publicly available to researchers, app developers, and smartphone manufacturers through multiple channels (SQL, REST API, zipped CSV/Parquet dump). Results This paper presents the results of a wide analysis of the tendency several smart-phone characteristics have on the battery charge/discharge rate, such as the different models, brands, networks, settings, applications, and even countries. Our analysis was performed over the crowdsourced data, and we have presented findings such as which applications tend to be around when battery consumption is the highest, do users from different countries have the same battery usage, and even showcase methods to help developers find and improve energy inefficient processes. The dataset we considered is sizable; it comprises 23+ million (anonymous) data samples stemming from a large number of installations of the mobile app. Moreover, it includes 700+ million data points pertaining to processes running on these devices. In addition, the dataset is diverse. It covers 1.6k+ device brands, 11.8k+ smartphone models, and more than 50 Android versions. We have been using this dataset to perform multiple analyses. For example, we studied what are the most common apps running on these smartphones and related the presence of those apps in memory with the battery discharge rate of these devices. We have also used this dataset in teaching, having had students practicing data analysis and machine learning techniques for relating energy consumption/charging rates with many other hardware and software qualities, attributes and user behaviors. Conclusions The dataset we considered can support studies with a wide range of research goals, be those energy efficiency or not. It opens the opportunity to inform and reshape user habits, and even influence the development of both hardware (manufacturers) and software (developers) for mobile devices. Our analysis also shows results which go outside of the common perception of what impacts battery consumption in real-world usage, while exposing new varied, complex, and promising research avenues.

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.

2020

On energy debt: managing consumption on evolving software

Authors
Couto, M; Maia, D; Saraiva, J; Pereira, R;

Publication
TechDebt '20: International Conference on Technical Debt, Seoul, Republic of Korea, June 28-30, 2020

Abstract