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Facts & Numbers
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Presentation

High-Assurance Software

HASLab is focused on the design and implementation of high-assurance software systems: software that is correct by design and resilient to environment faults and malicious attacks. 

To accomplish this mission, HASLab covers three main competences — Cybersecurity, Distributed Systems, and Software Engineering — complemented by other competences such as Human-Computer Interaction, Programming Languages, or the Mathematics of Computing. 

Software Engineering – methods, techniques, and tools for rigorous software development, that can be applied to the internal functionality of a component, its composition with other components, as well as the interaction with the user.

Distributed Systems – improving the reliability and scalability of software, by exploring properties inherent to the distribution and replication of computer systems.

Cybersecurity – minimize the vulnerability of software components to hostile attacks, by deploying structures and cryptographic protocols whose security properties are formally proven.

Through a multidisciplinary approach that is based on solid theoretical foundations, we aim to provide solutions — theory, methods, languages, tools — for the development of complete ICT systems that provide strong guarantees to their owners and users. Prominent application areas of HASLab research include the development of safety and security critical software systems, the operation of secure cloud infrastructures, and the privacy-preserving management and processing of big data.

Latest News
Computer Science and Engineering

There are bridges uniting biomedical engineering and supercomputing - INESC TEC researchers flew to Barcelona to cross them

For a week, Alicia Oliveira and Beatriz Cepa left INESC TEC's laboratories in Braga and went to Barcelona - the city that welcomed the ACM Summer School. The researchers explored some of the elemental HPC concepts and realised that - in a context dominated by computer science - their training in biomedical engineering was an asset.

31st October 2024

Computer Science and Engineering

Software bugs are as persistent as those in nature - a study by INESC TEC closed in on them

INESC TEC researchers developed the LazyFS tool, capable of injecting faults and reproducing data loss bugs. The solution helps to understand the origin and cause of said bugs, but also to validate protection mechanisms against failures. 

07th October 2024

In the era of pervading data storage, replication can be the key to large-scale systems. Here’s how a INESC TEC research explores these challenges

In a study published in ACM Computing Surveys, Paulo Sérgio Almeida, INESC TEC researcher, synthesises the existing knowledge on approaches to Conflict-free Replicated Data Types, a topic he has been exploring over the past decade. These enable replication in distributed systems with automatic conflict resolution, ensuring high availability – even in the face of communication failures.

04th October 2024

INESC TEC with five FCT exploratory projects approved in four R&D areas

Telecommunications and Multimedia, Applied Photonics, High-assurance Software and Advanced Computing Systems – these are the four domains that INESC TEC researchers will explore within the scope of the five projects that were approved through the Call for Exploratory Projects promoted by the Foundation for Science and Technology (FCT).

02nd October 2024

Computer Science and Engineering

Edge databases have many benefits — and INESC TEC researchers have dedicated themselves to studying them

The paper Databases in Edge and Fog Environments: A Survey, signed by Luís Manuel Ferreira, Fábio Coelho and José Orlando Pereira - and published in ACM Computing Surveys -, establishes innovative concepts in the edge databases area, resorting to several publications on hardware used, latency performance, energy consumption and privacy. This new type of database benefits from devices close to the users to improve performance and features.

03rd July 2024

057

Projects

CDMS

Claim Denial Management Solution

2025-2026

exaSIMPLE

exaSIMPLE: A Hybrid ML-CFD SIMPLE Algorithm for the Exascale Era

2024-2025

EPICURE

High-level specialised application support service in High-Performance Computing (HPC)

2024-2028

BCDSM

BCD.S+M - Modular Blockchain Data Storage and Management System with AI

2024-2027

TwinEU

Digital Twin for Europe

2024-2026

HEDGE_IoT

Holistic Approach towards Empowerment of the DiGitalization of the Energy Ecosystem through adoption of IoT solutions

2024-2027

HANAMI

Hpc AlliaNce for Applications and supercoMputing Innovation: the Europe - Japan collaboration

2024-2026

AzDIH

Azores Digital Innovation Hub on Tourism and Sustainability

2023-2025

PFAI4_4eD

Programa de Formação Avançada Industria 4 - 4a edição

2023-2023

ATE

Alliance for Energy Transition

2023-2025

Green_Dat_AI

Energy-efficient AI-ready Data Spaces

2023-2025

EuroCC2

National Competence Centres in the framework of EuroHPC Phase 2

2023-2025

AURORA

Deteção de atividade no interior do veículo

2022-2023

ATTRACT_DIH

Digital Innovation Hub for Artificial Intelligence and High-Performance Computing

2022-2025

NewSpacePortugal

Agenda New Space Portugal

2022-2025

BeFlexible

Boosting engagement to increase flexibility

2022-2026

ENERSHARE

European commoN EneRgy dataSpace framework enabling data sHaring-driven Across- and beyond- eneRgy sErvices

2022-2025

Gridsoft

Parecer sobre a implementação de software para redes elétricas inteligentes

2022-2022

PFAI4_3ed

Programa de Formação Avançada Industria 4 - 3a edição

2022-2022

THEIA

Automated Perception Driving

2022-2023

IBEX

Métodos quantitativos para a programação ciber-física: Uma abordagem precisa para racicionar sobre imprecisões na computação ciber-física

2022-2025

SpecRep

Constraint-based Specification Repair

2022-2023

FLEXCOMM

Towards Energy-aware Communications: Connecting the power grid and communication infrastructure

2022-2023

Sustainable HPC

Computação de elevado desempenho sustentável

2021-2025

CircThread

Building the Digital Thread for Circular Economy Product, Resource & Service Management

2021-2025

PassCert

Exploring the Impact of Formal Verification on the Adoption of Password Security Software

2021-2022

IoT4Distribuicao

Análise de Requisitos e Especificação Funcional de uma Arquitetura Distribuída baseada em soluções IoT para a Gestão e Controlo da Rede de Distribuição

2021-2023

RISC2

A network for supporting the coordination of High-Performance Computing research between Europe and Latin America

2021-2023

PFAI4.0

Programa de Formação Avançada Industria 4.0

2020-2021

PAStor

Programmable and Adaptable Storage for AI-oriented HPC Ecosystems

2020-2021

ACTPM

Automating Crash-Consistency Testing for Persistent Memory

2020-2021

AIDA

Adaptive, Intelligent and Distributed Assurance Platform

2020-2023

BigHPC

A Management Framework for Consolidated Big Data and HPC

2020-2023

SLSNA

Prestação de Serviços no ambito do projeto SKORR

2020-2021

InterConnect

Interoperable Solutions Connecting Smart Homes, Buildings and Grids

2019-2024

T4CDTKC

Training 4 Cotec, Digital Transformation Knowledge Challenge - Elaboração de Programa de Formação “CONHECER E COMPREENDER O DESAFIO DAS TECNOLOGIAS DE TRANSFORMAÇÃO DIGITAL”

2019-2021

CLOUD4CANDY

Cloud for CANDY

2019-2019

HADES

HArdware-backed trusted and scalable DEcentralized Systems

2018-2022

MaLPIS

Aprendizagem Automática para Deteção de Ataques e Identificação de Perfis Segurança na Internet

2018-2022

SKORR

Advancing the Frontier of Social Media Management Tools

2018-2021

DaVinci

Distributed architectures: variability and interaction for cyber-physical systems

2018-2022

SAFER

Safery verification for robotic software

2018-2021

KLEE

Coalgebraic modeling and analysis for computational synthetic biology

2018-2021

InteGrid

Demonstration of INTElligent grid technologies for renewables INTEgration and INTEractive consumer participation enabling INTEroperable market solutions and INTErconnected stakeholders

2017-2020

Lightkone

Lightweight Computation for Networks at the Edge

2017-2019

CloudDBAppliance

European Cloud In-Memory Database Appliance with Predictable Performance for Critical Applications

2016-2019

Cloud-Setup

PLATAFORMA DE PREPARAÇÃO DE CONTEÚDOS AUDIOVISUAIS PARA INGEST NA CLOUD

2016-2019

GSL

GreenSoftwareLab: Towards an Engineering Discipline for Green Software

2016-2019

CORAL-TOOLS

CORAL – Sustainable Ocean Exploitation: Tools and Sensors

2016-2018

SafeCloud

Secure and Resilient Cloud Architecture

2015-2018

NanoStima-RL1

NanoSTIMA - Macro-to-Nano Human Sensing Technologies

2015-2019

NanoStima-RL3

NanoSTIMA - Health data infrastructure

2015-2019

SMILES

SMILES - Smart, Mobile, Intelligent and Large scale Sensing and analytics

2015-2019

UPGRID

Real proven solutions to enable active demand and distributed generation flexible integration, through a fully controllable LOW Voltage and medium voltage distribution grid

2015-2017

LeanBigData

Ultra-Scalable and Ultra-Efficient Integrated and Visual Big Data Analytics

2014-2017

Practice

Privacy-Preserving Computation in the Cloud

2013-2016

CoherentPaaS

A Coherent and Rich PaaS with a Common Programming Model

2013-2016

Team
001

Laboratory

CLOUDinha

Publications

HASLab Publications

View all Publications

2025

Specification of paraconsistent transition systems, revisited

Authors
Cunha, J; Madeira, A; Barbosa, LS;

Publication
SCIENCE OF COMPUTER PROGRAMMING

Abstract
The need for more flexible and robust models to reason about systems in the presence of conflicting information is becoming more and more relevant in different contexts. This has prompted the introduction of paraconsistent transition systems, where transitions are characterized by two pairs of weights: one representing the evidence that the transition effectively occurs and the other its absence. Such a pair of weights can express scenarios of vagueness and inconsistency. . This paper establishes a foundation for a compositional and structured specification approach of paraconsistent transition systems, framed as paraconsistent institution. . The proposed methodology follows the stepwise implementation process outlined by Sannella and Tarlecki.

2025

Approaches to Conflict-free Replicated Data Types

Authors
Almeida, PS;

Publication
ACM COMPUTING SURVEYS

Abstract
Conflict-free Replicated Data Types (CRDTs) allow optimistic replication in a principled way. Different replicas can proceed independently, being available even under network partitions and always converging deterministically: Replicas that have received the same updates will have equivalent state, even if received in different orders. After a historical tour of the evolution from sequential data types to CRDTs, we present in detail the two main approaches to CRDTs, operation-based and state-based, including two important variations, the pure operation-based and the delta-state based. Intended for prospective CRDT researchers and designers, this article provides solid coverage of the essential concepts, clarifying some misconceptions that frequently occur, but also presents some novel insights gained from considerable experience in designing both specific CRDTs and approaches to CRDTs.

2025

Alloy Repair Hint Generation Based on Historical Data

Authors
Barros, A; Neto, H; Cunha, A; Macedo, N; Paiva, ACR;

Publication
FORMAL METHODS, PT II, FM 2024

Abstract
Platforms to support novices learning to program are often accompanied by automated next-step hints that guide them towards correct solutions. Many of those approaches are data-driven, building on historical data to generate higher quality hints. Formal specifications are increasingly relevant in software engineering activities, but very little support exists to help novices while learning. Alloy is a formal specification language often used in courses on formal software development methods, and a platform-Alloy4Fun-has been proposed to support autonomous learning. While non-data-driven specification repair techniques have been proposed for Alloy that could be leveraged to generate next-step hints, no data-driven hint generation approach has been proposed so far. This paper presents the first data-driven hint generation technique for Alloy and its implementation as an extension to Alloy4Fun, being based on the data collected by that platform. This historical data is processed into graphs that capture past students' progress while solving specification challenges. Hint generation can be customized with policies that take into consideration diverse factors, such as the popularity of paths in those graphs successfully traversed by previous students. Our evaluation shows that the performance of this new technique is competitive with non-data-driven repair techniques. To assess the quality of the hints, and help select the most appropriate hint generation policy, we conducted a survey with experienced Alloy instructors.

2024

Exploring Frama-C Resources by Verifying Space Software

Authors
Busquim e Silva, RA; Arai, NN; Burgareli, LA; Parente de Oliveira, JM; Sousa Pinto, J;

Publication
Computer Science Foundations and Applied Logic

Abstract

2024

Performance and explainability of feature selection-boosted tree-based classifiers for COVID-19 detection

Authors
Rufino, J; Ramírez, JM; Aguilar, J; Baquero, C; Champati, J; Frey, D; Lillo, RE; Fernández Anta, A;

Publication
HELIYON

Abstract
In this paper, we evaluate the performance and analyze the explainability of machine learning models boosted by feature selection in predicting COVID-19-positive cases from self-reported information. In essence, this work describes a methodology to identify COVID-19 infections that considers the large amount of information collected by the University of Maryland Global COVID-19 Trends and Impact Survey (UMD-CTIS). More precisely, this methodology performs a feature selection stage based on the recursive feature elimination (RFE) method to reduce the number of input variables without compromising detection accuracy. A tree-based supervised machine learning model is then optimized with the selected features to detect COVID-19-active cases. In contrast to previous approaches that use a limited set of selected symptoms, the proposed approach builds the detection engine considering a broad range of features including self-reported symptoms, local community information, vaccination acceptance, and isolation measures, among others. To implement the methodology, three different supervised classifiers were used: random forests (RF), light gradient boosting (LGB), and extreme gradient boosting (XGB). Based on data collected from the UMD-CTIS, we evaluated the detection performance of the methodology for four countries (Brazil, Canada, Japan, and South Africa) and two periods (2020 and 2021). The proposed approach was assessed in terms of various quality metrics: F1-score, sensitivity, specificity, precision, receiver operating characteristic (ROC), and area under the ROC curve (AUC). This work also shows the normalized daily incidence curves obtained by the proposed approach for the four countries. Finally, we perform an explainability analysis using Shapley values and feature importance to determine the relevance of each feature and the corresponding contribution for each country and each country/year.

Facts & Figures

68Researchers

2016

1R&D Employees

2020

0Book Chapters

2020

Contacts