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Human-Centered Computing and Information Science

HumanISE is an interdisciplinary centre focused on research at the forefront of human-centred computing (HCC) with broad and deep expertise in computer science (CS) and information science (IS).

At HumanISE, engineers, scientists and designers focus on research and development of software systems, methods, and tools, capable of leveraging human abilities and practices within their communities and environments, involving high technical and managerial complexity, due to large scale, high heterogeneity, high uncertainty, high integrity, strict compliance to standards and legal frameworks, or domain-specific organisational issues.

Our mission is to pursue high-quality research, innovation, consultancy, and technology transfer, impactful, in close cooperation with academic and industrial partners. We focus on five main research areas - Computer-Human Interaction, Computer Graphics and Interactive Digital Media, Information Management and Information Systems, Software Engineering, and Large Scale and Special Purpose Computing Systems, Languages and Tools - and four innovation areas - Personalised Health Research, Earth, Ocean and Space Science, Geospatial Information Systems Engineering, and Information Systems and Applied Computing.

Furthermore, at HumanISE, we are also strongly committed to training young researchers and professionals, with a significant track record in the supervision of master and PhD students.

Presently, our researchers originate from the University of Porto (UP), Polytechnic of Porto (IPP), University of Trás-os-Montes e Alto Douro (UTAD), Universidade Aberta (UAb) and University of Minho (UM).

Latest News
Computer Science and Engineering

Seniors training with Artificial Intelligence? The future (really is) today

The project is called IATOS and it aims to make remote physical training safer, more effective, and accessible for older adults. Led by a consortium including INESC TEC, AGIT TECH, and the University of Trás-os-Montes and Alto Douro, the initiative seeks to create a digital platform for AI-assisted physical training, specifically designed for people over 65.

29th September 2025

Power and Energy Systems

There are innovative technological solutions to be developed for local energy markets – with contributions from INESC TEC

The delay in generating synthetic data for time series – fundamental elements in energy forecasting scenarios – was one of the motivations for GENESIS, a project that aims to provide local electricity markets with contextual synthetic data and reliable artificial intelligence models.

22nd September 2025

Computer Science and Engineering

INESC TEC researchers organised an international conference on computer vision and computer graphics

The VISIGRAPP 2025 - 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - took place in Porto, in late February. It brought together more than 380 researchers and professionals interested in keeping up with and discussing theoretical advances and applications in Computer Vision, Computer Graphics, Human-Computer Interaction, and Information Visualisation - the main themes of the co-located conferences VISAPP, GRAPP, HUCAPP, and IVAPP, respectively. One of the co-chairs of this conference was INESC TEC researcher A. Augusto de Sousa.

27th March 2025

Computer Science and Engineering

Self-driving cars? Yes, but only under certain conditions - sustainable and adaptable, according to our researchers

FRODDO - this is the name of the project that will improve the safety and robustness of autonomous and connected systems, e.g., self-driving cars, through the development and testing of technological solutions, thus supporting user-centred mobility. INESC TEC is one of the partners of this European project, with a funding of almost six million euros, under the Horizon Europe programme.

07th February 2025

Computer Science and Engineering

Immersive learning arrived to influence educational contexts. And an INESC TEC researcher leads the main network in this domain

Daniela Pedrosa, INESC TEC's researcher, took on the position of editing manager responsible for the publications of the Immersive Learning Research Network (iLRN), after four years as publication chairon the organising committee of the conference promoted by the association. 

04th February 2025

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Featured Projects

AgroTecVerdeVR

Aplicação de Realidade Virtual para Visitas Virtuais

2025-2026

DUVOPS

Ocean Preservation System Based on Digital Twins and Fleets of Heterogeneous Unmanned Vehicles

2025-2028

XARPER_RETAIL

XARP Extended Reality for the Retail Industry

2025-2027

PGU2CIMTS

Consultoria para a implementação de uma Plataforma de Gestão Urbana na CIM do Tâmega e Sousa

2025-2025

IATriage4SU

Apoio à tomada de decisão na triagem de urgência de adultos, com recurso a inteligência artificial

2025-2026

SMARTBOGIE

SMARTBOGIE – Solução Inovadora para a Mobilidade Ferroviária de Mercadorias

2025-2028

BolsasFCT_Gestao

Funding FCT PhD Grants - Management

2025-9999

AMIDA

Aquisição de um serviço de desenvolvimento de arquitetura do sistema AMIDA

2024-2025

PFAI4_5eD

Programa de Formação Avançada Industria 4 - 5a edição

2024-2024

Data4LEM

Synthetic and Explainable Data Generation for the Simulation and Analysis of Future Local Electricity Markets

2024-2025

PTPumpup

Building Portuguese Language Resources through machine learning and limited human interaction

2021-2024

RTE

2015-2016

Team
003

Laboratories

Information Systems Laboratory

Laboratory of Software Engineering

Laboratory of Computer Graphics and Virtual Environments

Publications

HumanISE Publications

View all Publications

2026

An Explosion of the Uses of Immersive Learning Environments: A Mapping of Reviews Update

Authors
Beck, E; Morgado, LC; O’Shea, M;

Publication
Communications in Computer and Information Science

Abstract
Since the publication of the 2020 paper, “Finding the Gaps About Uses of Immersive Learning Environments: A Survey of Surveys,” the landscape of immersive learning environments (ILEs) has continued to evolve rapidly. This update aims to revisit the gaps identified in that previous research and explore emerging trends. We conducted an extensive review of new surveys published after that paper’s cut date. Our findings reveal a significant amount of new published reviews (n?=?64), more than doubling the original corpus (n?=?47). The results highlighted novel themes of usage of immersive environments, helping bridge some 2020 research gaps. This paper discusses those developments and presents a consolidated perspective on the uses of immersive learning environments. © 2025 Elsevier B.V., All rights reserved.

2026

A framework for supporting the reproducibility of computational experiments in multiple scientific domains

Authors
Costa, L; Barbosa, S; Cunha, J;

Publication
Future Gener. Comput. Syst.

Abstract
In recent years, the research community, but also the general public, has raised serious questions about the reproducibility and replicability of scientific work. Since many studies include some kind of computational work, these issues are also a technological challenge, not only in computer science, but also in most research domains. Computational replicability and reproducibility are not easy to achieve due to the variety of computational environments that can be used. Indeed, it is challenging to recreate the same environment via the same frameworks, code, programming languages, dependencies, and so on. We propose a framework, known as SciRep, that supports the configuration, execution, and packaging of computational experiments by defining their code, data, programming languages, dependencies, databases, and commands to be executed. After the initial configuration, the experiments can be executed any number of times, always producing exactly the same results. Our approach allows the creation of a reproducibility package for experiments from multiple scientific fields, from medicine to computer science, which can be re-executed on any computer. The produced package acts as a capsule, holding absolutely everything necessary to re-execute the experiment. To evaluate our framework, we compare it with three state-of-the-art tools and use it to reproduce 18 experiments extracted from published scientific articles. With our approach, we were able to execute 16 (89%) of those experiments, while the others reached only 61%, thus showing that our approach is effective. Moreover, all the experiments that were executed produced the results presented in the original publication. Thus, SciRep was able to reproduce 100% of the experiments it could run. © 2025 The Authors

2025

LLM Prompt Engineering for Automated White-Box Integration Test Generation in REST APIs

Authors
Rincon, AM; Vincenzi, AMR; Faria, JP;

Publication
2025 IEEE INTERNATIONAL CONFERENCE ON SOFTWARE TESTING, VERIFICATION AND VALIDATION WORKSHOPS, ICSTW

Abstract
This study explores prompt engineering for automated white-box integration testing of RESTful APIs using Large Language Models (LLMs). Four versions of prompts were designed and tested across three OpenAI models (GPT-3.5 Turbo, GPT-4 Turbo, and GPT-4o) to assess their impact on code coverage, token consumption, execution time, and financial cost. The results indicate that different prompt versions, especially with more advanced models, achieved up to 90% coverage, although at higher costs. Additionally, combining test sets from different models increased coverage, reaching 96% in some cases. We also compared the results with EvoMaster, a specialized tool for generating tests for REST APIs, where LLM-generated tests achieved comparable or higher coverage in the benchmark projects. Despite higher execution costs, LLMs demonstrated superior adaptability and flexibility in test generation.

2025

Automated Social Media Feedback Analysis for Software Requirements Elicitation: A Case Study in the Streaming Industry

Authors
Silva, M; Faria, JP;

Publication
Proceedings of the 20th International Conference on Evaluation of Novel Approaches to Software Engineering, ENASE 2025, Porto, Portugal, April 4-6, 2025.

Abstract

2025

Automatic Generation of Loop Invariants in Dafny with Large Language Models

Authors
Faria, JP; Trigo, E; Abreu, R;

Publication
FUNDAMENTALS OF SOFTWARE ENGINEERING, FSEN 2025

Abstract
Recent verification tools aim to make formal verification more accessible for software engineers by automating most of the verification process. However, the manual work and expertise required to write verification helper code, such as loop invariants and auxiliary lemmas and assertions, remains a barrier. This paper explores the use of Large Language Models (LLMs) to automate the generation of loop invariants for programs in Dafny. We tested the approach on a curated dataset of 100 programs in Dafny involving arrays, strings, and numeric types. Using a multimodel approach that combines GPT-4o and Claude 3.5 Sonnet, correct loop invariants (passing the Dafny verifier) were generated at the first attempt for 92% of the programs, and in at most five attempts for 95% of the programs. Additionally, we developed an extension to the Dafny plugin for Visual Studio Code to incorporate automatic loop invariant generation into the IDE. Our work stands out from related approaches by handling a broader class of problems and offering IDE integration.