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Sobre

Sobre

João Pascoal Faria tem um doutoramento em Engenharia Electrotécnica e de Computadores pela Faculdade de Engenharia da Universidade do Porto em 1999, onde é atualmente Professor Associado no Departamento de Engenharia Informática e Diretor do Mestrado Integrado em Engenharia Informática e Computação. É membro do Grupo de Investigação em Engenharia de Software (softeng.fe.up.pt) e investigador do INESC TEC, onde coordena a área de Engenharia de Software. Representa a FEUP e o INESC TEC na Comissão Técnica de Sistemas de Informação para a Saúde (CT 199) e a FEUP como Presidente da Comissão Setorial para a Qualidade das Tecnologia da Informação e das Comunicações (CS/03), no âmbito do Instituto Português da Qualidade (IPQ). No passado, trabalhou com várias empresas de software (Novabase Saúde, Sidereus, Medidata) e foi co-fundador de outras duas (QualiSoft e Strongstep). Tem mais de 25 anos de experiência em ensino, investigação, desenvolvimento e consultoria em diversas áreas de engenharia de software. É o principal autor de uma ferramenta de desenvolvimento rápido de aplicações (SAGA), com base em linguagens específicas de domínio, com mais de 25 anos de presença no mercado e evolução (1989-presente). Está atualmente envolvido em projectos de investigação, supervisões e atividades de consultoria nas áreas de teste de software baseado em modelos, melhoria de processos de software e desenvolvimento conduzido por modelos.

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    João Pascoal Faria
  • Cargo

    Investigador Sénior
  • Desde

    14 outubro 1985
001
Publicações

2023

Applying Machine Learning to Estimate the Effort and Duration of Individual Tasks in Software Projects

Autores
Sousa, AO; Veloso, DT; Goncalves, HM; Faria, JP; Mendes Moreira, J; Graca, R; Gomes, D; Castro, RN; Henriques, PC;

Publicação
IEEE ACCESS

Abstract
Software estimation is a vital yet challenging project management activity. Various methods, from empirical to algorithmic, have been developed to fit different development contexts, from plan-driven to agile. Recently, machine learning techniques have shown potential in this realm but are still underexplored, especially for individual task estimation. We investigate the use of machine learning techniques in predicting task effort and duration in software projects to assess their applicability and effectiveness in production environments, identify the best-performing algorithms, and pinpoint key input variables (features) for predictions. We conducted experiments with datasets of various sizes and structures exported from three project management tools used by partner companies. For each dataset, we trained regression models for predicting the effort and duration of individual tasks using eight machine learning algorithms. The models were validated using k-fold cross-validation and evaluated with several metrics. Ensemble algorithms like Random Forest, Extra Trees Regressor, and XGBoost consistently outperformed non-ensemble ones across the three datasets. However, the estimation accuracy and feature importance varied significantly across datasets, with a Mean Magnitude of Relative Error (MMRE) ranging from 0.11 to 9.45 across the datasets and target variables. Nevertheless, even in the worst-performing dataset, effort estimates aggregated to the project level showed good accuracy, with MMRE = 0.23. Machine learning algorithms, especially ensemble ones, seem to be a viable option for estimating the effort and duration of individual tasks in software projects. However, the quality of the estimates and the relevant features may depend largely on the characteristics of the available datasets and underlying projects. Nevertheless, even when the accuracy of individual estimates is poor, the aggregated estimates at the project level may present a good accuracy due to error compensation.

2023

Case studies of development of verified programs with Dafny for accessibility assessment

Autores
Faria, JP; Abreu, R;

Publicação
CoRR

Abstract

2023

Case Studies of Development of Verified Programs with Dafny for Accessibility Assessment

Autores
Faria, JP; Abreu, R;

Publicação
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract
Formal verification techniques aim at formally proving the correctness of a computer program with respect to a formal specification, but the expertise and effort required for applying formal specification and verification techniques and scalability issues have limited their practical application. In recent years, the tremendous progress with SAT and SMT solvers enabled the construction of a new generation of tools that promise to make formal verification more accessible for software engineers, by automating most if not all of the verification process. The Dafny system is a prominent example of that trend. However, little evidence exists yet about its accessibility. To help fill this gap, we conducted a set of 10 case studies of developing verified implementations in Dafny of some real-world algorithms and data structures, to determine its accessibility for software engineers. We found that, on average, the amount of code written for specification and verification purposes is of the same order of magnitude as the traditional code written for implementation and testing purposes (ratio of 1.14) – an “overhead” that certainly pays off for high-integrity software. The performance of the Dafny verifier was impressive, with 2.4 proof obligations generated per line of code written, and 24 ms spent per proof obligation generated and verified, on average. However, we also found that the manual work needed in writing auxiliary verification code may be significant and difficult to predict and master. Hence, further automation and systematization of verification tasks are possible directions for future advances in the field. © 2023, IFIP International Federation for Information Processing.

2023

Case Studies of Development of Verified Programs with Dafny for Accessibility Assessment

Autores
Faria, JP; Abreu, R;

Publicação
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract
Formal verification techniques aim at formally proving the correctness of a computer program with respect to a formal specification, but the expertise and effort required for applying formal specification and verification techniques and scalability issues have limited their practical application. In recent years, the tremendous progress with SAT and SMT solvers enabled the construction of a new generation of tools that promise to make formal verification more accessible for software engineers, by automating most if not all of the verification process. The Dafny system is a prominent example of that trend. However, little evidence exists yet about its accessibility. To help fill this gap, we conducted a set of 10 case studies of developing verified implementations in Dafny of some real-world algorithms and data structures, to determine its accessibility for software engineers. We found that, on average, the amount of code written for specification and verification purposes is of the same order of magnitude as the traditional code written for implementation and testing purposes (ratio of 1.14) – an “overhead” that certainly pays off for high-integrity software. The performance of the Dafny verifier was impressive, with 2.4 proof obligations generated per line of code written, and 24 ms spent per proof obligation generated and verified, on average. However, we also found that the manual work needed in writing auxiliary verification code may be significant and difficult to predict and master. Hence, further automation and systematization of verification tasks are possible directions for future advances in the field. © 2023, IFIP International Federation for Information Processing.

2023

Towards Computer Assisted Compliance Assessment in the Development of Software as a Medical Device

Autores
Farshid, S; Lima, B; Faria, JP;

Publicação
Proceedings of the 18th International Conference on Software Technologies, ICSOFT 2023, Rome, Italy, July 10-12, 2023.

Abstract

Teses
supervisionadas

2022

A Pattern-based Test Platform for IoT and eHealth

Autor
Pedro José Brandão Almeida

Instituição
UP-FEUP

2022

Increasing the Dependability of Internet-of-Things Systems in the context of End-User Development Environments

Autor
João Pedro Matos Teixeira Dias

Instituição
UP-FEUP

2022

Low-Code Data Model Designer

Autor
Ana Isabel Ferreira Maia

Instituição
UP-FEUP

2022

Integration of Fraud Detection Services in Payment Processing Systems

Autor
Ana Margarida Ruivo Loureiro

Instituição
UP-FEUP

2021

Increasing the Dependability of Internet-of-Things Systems in the context of End-User Development Environments

Autor
João Pedro Matos Teixeira Dias

Instituição
UP-FEUP