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About

Currently, I'm an Associate Professor at the Faculty of Engineering at University of Porto and an integrated member of the research center HASLab/INESC TEC. My research is mainly focused on aiding end users being more effective and efficient when developing their software systems. In particular, I've made several contributions to improve the effectiveness and efficiency of spreadsheet users/developers. More recently I've also worked on improving software energy consumption through the analysis and transformation of its source code. Currently I'm focused on making data science more accessible for (end) users. Moreover, I'm also studying how to improve the development of software applications based on the microservices architectural style. I obtained my PhD degree in Computer Science from the University of Minho in 2011. Afterward, I was a postdoctoral fellow jointly at University of Minho and Oregon State University, and was also an Invited Professor at School of Management and Technology of Polytechnic Institute of Porto, Assistant Professor at NOVA University of Lisbon and Assistant Professor at University of Minho.

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Details

Details

  • Name

    Jácome Costa Cunha
  • Cluster

    Computer Science
  • Role

    Researcher
  • Since

    01st November 2011
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

Identification of microservices from monolithic applications through topic modelling

Authors
Brito, M; Cunha, J; Saraiva, J;

Publication
SAC '21: The 36th ACM/SIGAPP Symposium on Applied Computing, Virtual Event, Republic of Korea, March 22-26, 2021

Abstract
Microservices emerged as one of the most popular architectural patterns in the recent years given the increased need to scale, grow and flexibilize software projects accompanied by the growth in cloud computing and DevOps. Many software applications are being submitted to a process of migration from its monolithic architecture to a more modular, scalable and flexible architecture of microservices. This process is slow and, depending on the project's complexity, it may take months or even years to complete. This paper proposes a new approach on microservice identification by resorting to topic modelling in order to identify services according to domain terms. This approach in combination with clustering techniques produces a set of services based on the original software. The proposed methodology is implemented as an open-source tool for exploration of monolithic architectures and identification of microservices. A quantitative analysis using the state of the art metrics on independence of functionality and modularity of services was conducted on 200 open-source projects collected from GitHub. Cohesion at message and domain level metrics' showed medians of roughly 0.6. Interfaces per service exhibited a median of 1.5 with a compact interquartile range. Structural and conceptual modularity revealed medians of 0.2 and 0.4 respectively. Our first results are positive demonstrating beneficial identification of services due to overall metrics' results. © 2021 ACM.

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 Understanding Data Scientists

Authors
Pereira, P; Cunha, J; Fernandes, JP;

Publication
2020 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)

Abstract

2020

Data Curation: Towards a Tool for All

Authors
Dias, J; Cunha, J; Pereira, R;

Publication
HCI International 2020 - Late Breaking Posters - 22nd International Conference, HCII 2020, Copenhagen, Denmark, July 19-24, 2020, Proceedings, Part I

Abstract

Supervised
thesis

2020

Humanized Data Cleaning

Author
José Miguel Silva Dias

Institution
UM

2020

Resiliência em soluções orientadas aos microsserviços

Author
Marco António Rodrigues Oliveira Silva

Institution
UM

2020

Identification of Microservices from Monolithic Applications through Topic Modelling

Author
Miguel António Ferrão Brito

Institution
UM

2018

Energyware Engineering: Techniques and Tools for Green Software Development

Author
Rui Alexandre Afonso Pereira

Institution
Outra

2018

Design, Implementation and Evaluation of Model-Driven Spreadsheets

Author
Jorge Cunha Mendes

Institution
UM