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Publicações

Publicações por Marco Linhares Couto

2015

Automatic Distinction of Fernando Pessoas' Heteronyms

Autores
Teixeira, JF; Couto, M;

Publicação
PROGRESS IN ARTIFICIAL INTELLIGENCE

Abstract
Text Mining has opened a vast array of possibilities concerning automatic information retrieval from large amounts of text documents. A variety of themes and types of documents can be easily analyzed. More complex features such as those used in Forensic Linguistics can gather deeper understanding from the documents, making possible performing difficult tasks such as author identification. In this work we explore the capabilities of simpler Text Mining approaches to author identification of unstructured documents, in particular the ability to distinguish poetic works from two of Fernando Pessoas' heteronyms: 'Alvaro de Campos and Ricardo Reis. Several processing options were tested and accuracies of 97% were reached, which encourage further developments.

2015

GreenDroid: A Tool for Analysing Power Consumption in the Android Ecosystem

Autores
Couto, M; Cunha, J; Fernandes, JP; Pereira, R; Saraiva, J;

Publicação
2015 IEEE 13th International Scientific Conference on Informatics

Abstract
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.

2017

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

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

Publicação
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.

2014

Detecting Anomalous Energy Consumption in Android Applications

Autores
Couto, M; Carcao, T; Cunha, J; Fernandes, JP; Saraiva, J;

Publicação
PROGRAMMING LANGUAGES, SBLP 2014

Abstract
The use of powerful mobile devices, like smartphones, tablets and laptops, is changing the way programmers develop software. While in the past the primary goal to optimize software was the run time optimization, nowadays there is a growing awareness of the need to reduce energy consumption. This paper presents a technique and a tool to detect anomalous energy consumption in Android applications, and to relate it directly with the source code of the application. We propose a dynamically calibrated model for energy consumption for the Android ecosystem that supports different devices. The model is used as an API to monitor the application execution: first, we instrument the application source code so that we can relate energy consumption to the application source code; second, we use a statistical approach, based on fault-localization techniques, to localize abnormal energy consumption in the source code.

2017

Energy efficiency across programming languages: how do energy, time, and memory relate?

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

Publicação
Proceedings of the 10th ACM SIGPLAN International Conference on Software Language Engineering, SLE 2017, Vancouver, BC, Canada, October 23-24, 2017

Abstract

2016

The influence of the Java collection framework on overall energy consumption

Autores
Pereira, R; Couto, M; Saraiva, J; Cunha, J; Fernandes, JP;

Publicação
Proceedings of the 5th International Workshop on Green and Sustainable Software, GREENS@ICSE 2016, Austin, Texas, USA, May 16, 2016

Abstract
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. © 2016 ACM.

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