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About

João Pascoal Faria holds a PhD in Electrical and Computer Engineering from the Faculty of Engineering of the University of Porto in 1999, where he is currently Associate Professor at the Department of Informatics Engineering and Director of the Integrated Master in Informatics and Computing Engineering (MIEIC). He his a member of the Software Engineering Research Group (softeng.fe.up.pt) and researcher at INESC TEC, where he coordinates the Software Engineering area. He represents FEUP and INESC TEC in the Technical Comission for Health Informatics (CT 199) and FEUP as President of the Sectorial Comission for the Quality of Information and Communications Technology (CS/03), in the scope of the Portuguese Quality Institute (IPQ). In the past, he worked with several software companies (Novabase Saúde, Sidereus, Medidata) and was a co-founder of two other (Qualisoft and Strongstep). He has more than 25 years of experience in education, research, development and consultancy in several software engineering areas. He is the main author of a rapid application development tool (SAGA), based on domain specific languages, with more than 25 years of market presence and evolution (1989-present). He is currently involved in research projects, supervisions and consulting activities in the areas of model-based testing, software process improvement and model-driven development.

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Details

Details

  • Name

    João Pascoal Faria
  • Cluster

    Computer Science
  • Role

    Area Manager
  • Since

    14th October 1985
002
Publications

2020

Visual Self-healing Modelling for Reliable Internet-of-Things Systems

Authors
Dias, JP; Lima, B; Faria, JP; Restivo, A; Ferreira, HS;

Publication
Lecture Notes in Computer Science - Computational Science – ICCS 2020

Abstract

2020

Local Observability and Controllability Analysis and Enforcement in Distributed Testing with Time Constraints

Authors
Lima, B; Faria, JP; Hierons, R;

Publication
IEEE Access

Abstract

2020

The ProcessPAIR Method for Automated Software Process Performance Analysis

Authors
Raza, M; Faria, JP;

Publication
IEEE ACCESS

Abstract
High-maturity software development processes and development environments with automated data collection can generate significant amounts of data that can be periodically analyzed to identify performance problems, determine their root causes, and devise improvement actions. However, conducting the analysis manually is challenging because of the potentially large amount of data to analyze, the effort and expertise required, and the lack of benchmarks for comparison. In this article, we present ProcessPAIR, a novel method with tool support designed to help developers analyze their performance data with higher quality and less effort. Based on performance models structured manually by process experts and calibrated automatically from the performance data of many process users, it automatically identifies and ranks performance problems and potential root causes of individual subjects, so that subsequent manual analysis for the identification of deeper causes and improvement actions can be appropriately focused. We also show how ProcessPAIR was successfully instantiated and used in software engineering education and training, helping students analyze their performance data with higher satisfaction (by 25%), better quality of analysis outcomes (by 7%), and lower effort (by 4%), as compared to a traditional approach (with reduced tool support).

2020

A living lab for professional skills development in Software Engineering Management at U.Porto

Authors
Gonçalves, GM; Meneses, R; Faria, JP; Vidal, RM;

Publication
2020 IEEE Global Engineering Education Conference, EDUCON 2020, Porto, Portugal, April 27-30, 2020

Abstract

2019

Automatic calibration of performance indicators for performance analysis in software development

Authors
Raza, M; Faria, JP;

Publication
Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE

Abstract
ProcessPAIR is a novel method and tool for automating the performance analysis in software development. Based on performance models structured by process experts and calibrated from the performance data of many developers, it automatically identifies and ranks potential performance problems and root causes of individual developers. However, the current calibration method is not fully automatic, because, in the case of performance indicators that affect other indicators in a conflicting way, the process expert has to manually calibrate the optimal value in a way that balances those impacts. In this paper we propose a novel method to automate this step, taking advantage of training data sets. We demonstrate the feasibility of the method with an example related with the Code Review Rate indicator, with conflicting impacts on Productivity and Quality.

Supervised
thesis

2019

Aprendizagem Computacional para Gestão de Incidentes em Tecnologia da Informação

Author
Leonardo de Jesus Macedo

Institution
UP-FEUP

2019

Automated Scenario-based Testing of Distributed and Heterogeneous Systems

Author
Bruno Miguel Carvalhido Lima

Institution
UP-FEUP

2019

A Live Approach for Developing Internet-of-Things Systems

Author
João Pedro Matos Teixeira Dias

Institution
UP-FEUP

2019

Low-Code Solution for IoT Testing

Author
Hugo Diogo Queirós Cunha

Institution
UP-FEUP

2019

Análise e Melhoria de um Processo de Garantia de Qualidade de Software em Contexto Empresarial

Author
Miguel Lira Barbeitos Luís

Institution
UP-FEUP