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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

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

Computer Science and Engineering

Researcher at INESC TEC wins an award thanks to prototype to support the development of Defense policies and strategies

Alexandra Mendes, INESC TEC researcher, is one of the winners of the Atlantic Security Award, promoted by FLAD; Alexandra’s work aims to use a large language model – trained with data retrieved from the dark web – in decision-making processes, in the design of Defense policies and strategies, and in the application of the law in the security of the Atlantic region.

28th May 2024

Computer Science and Engineering

INESC TEC researchers propose innovative cryptography solution for potential quantum computers threats

The solution proposed by Manuel Barbosa and João Barbosa, researchers at INESC TEC, features a hybrid key-encapsulation mechanism (KEM), capable of addressing the demands of the dynamics of hybrid models that combine pre-quantum and post-quantum algorithms.

21st May 2024

Computer Science and Engineering

INESC TEC joins new collaboration project to bring Europe and Japan closer together in supercomputing

Medicine, climate, quantum physics or materials science. These are just few of the areas where supercomputing and modelling can play a major role in scientific, industrial, and social development. To strategically improve cooperation between Europe and Japan in this area of research, the HANAMI - HPC Alliance for Applications and Supercomputing Innovation: the Europe-Japan Collaboration project was born.

17th May 2024

Computer Science and Engineering

INESC TEC research on the application of Boolean functions in quantum computing integrates major publication

INESC TEC researchers developed a study to test the ability to perform any Boolean function on quantum computers designed according to a measurement-based quantum computation model.  

17th May 2024

053

Projects

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

TwinEU

Digital Twin for Europe

2024-2026

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

NewSpacePortugal

Agenda New Space Portugal

2022-2025

ATTRACT_DIH

Digital Innovation Hub for Artificial Intelligence and High-Performance Computing

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

SpecRep

Constraint-based Specification Repair

2022-2023

IBEX

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

2022-2024

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

PAStor

Programmable and Adaptable Storage for AI-oriented HPC Ecosystems

2020-2021

PFAI4.0

Programa de Formação Avançada Industria 4.0

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

GSL

GreenSoftwareLab: Towards an Engineering Discipline for Green Software

2016-2019

Cloud-Setup

PLATAFORMA DE PREPARAÇÃO DE CONTEÚDOS AUDIOVISUAIS PARA INGEST NA CLOUD

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

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.

2024

Pondering the Ugly Underbelly, and Whether Images Are Real

Authors
Hill, RK; Baquero, C;

Publication
Commun. ACM

Abstract
[No abstract available]

2024

A large-scale empirical study on mobile performance: energy, run-time and memory

Authors
Rua, R; Saraiva, J;

Publication
EMPIRICAL SOFTWARE ENGINEERING

Abstract
Software performance concerns have been attracting research interest at an increasing rate, especially regarding energy performance in non-wired computing devices. In the context of mobile devices, several research works have been devoted to assessing the performance of software and its underlying code. One important contribution of such research efforts is sets of programming guidelines aiming at identifying efficient and inefficient programming practices, and consequently to steer software developers to write performance-friendly code.Despite recent efforts in this direction, it is still almost unfeasible to obtain universal and up-to-date knowledge regarding software and respective source code performance. Namely regarding energy performance, where there has been growing interest in optimizing software energy consumption due to the power restrictions of such devices. There are still many difficulties reported by the community in measuring performance, namely in large-scale validation and replication. The Android ecosystem is a particular example, where the great fragmentation of the platform, the constant evolution of the hardware, the software platform, the development libraries themselves, and the fact that most of the platform tools are integrated into the IDE's GUI, makes it extremely difficult to perform performance studies based on large sets of data/applications. In this paper, we analyze the execution of a diversified corpus of applications of significant magnitude. We analyze the source-code performance of 1322 versions of 215 different Android applications, dynamically executed with over than 27900 tested scenarios, using state-of-the-art black-box testing frameworks with different combinations of GUI inputs. Our empirical analysis allowed to observe that semantic program changes such as adding functionality and repairing bugfixes are the changes more associated with relevant impact on energy performance. Furthermore, we also demonstrate that several coding practices previously identified as energy-greedy do not replicate such behavior in our execution context and can have distinct impacts across several performance indicators: runtime, memory and energy consumption. Some of these practices include some performance issues reported by the Android Lint and Android SDK APIs. We also provide evidence that the evaluated performance indicators have little to no correlation with the performance issues' priority detected by Android Lint. Finally, our results allowed us to demonstrate that there are significant differences in terms of performance between the most used libraries suited for implementing common programming tasks, such as HTTP communication, JSON manipulation, image loading/rendering, among others, providing a set of recommendations to select the most efficient library for each performance indicator. Based on the conclusions drawn and in the extension of the developed work, we also synthesized a set of guidelines that can be used by practitioners to replicate energy studies and build more efficient mobile software.

2024

Zipper-based embedding of strategic attribute grammars

Authors
Macedo, JN; Rodrigues, E; Viera, M; Saraiva, J;

Publication
JOURNAL OF SYSTEMS AND SOFTWARE

Abstract
Strategic term re-writing and attribute grammars are two powerful programming techniques widely used in language engineering. The former relies on strategies to apply term re-write rules in defining largescale language transformations, while the latter is suitable to express context-dependent language processing algorithms. These two techniques can be expressed and combined via a powerful navigation abstraction: generic zippers. This results in a concise zipper-based embedding offering the expressiveness of both techniques. In addition, we increase the functionalities of strategic programming, enabling the definition of outwards traversals; i.e. outside the starting position. Such elegant embedding has a severe limitation since it recomputes attribute values. This paper presents a proper and efficient embedding of both techniques. First, attribute values are memoized in the zipper data structure, thus avoiding their re-computation. Moreover, strategic zipper based functions are adapted to access such memoized values. We have hosted our memoized zipper-based embedding of strategic attribute grammars both in the Haskell and Python programming languages. Moreover, we benchmarked the libraries supporting both embedding against the state-of-the-art Haskell-based Strafunski and Scala-based Kiama libraries. The first results show that our Haskell Ztrategic library is very competitive against those two well established libraries.

2024

Assessing the impact of hints in learning formal specification

Authors
Cunha, A; Macedo, N; Campos, JC; Margolis, I; Sousa, E;

Publication
Proceedings of the 46th International Conference on Software Engineering: Software Engineering Education and Training, SEET@ICSE 2024, Lisbon, Portugal, April 14-20, 2024

Abstract
Background: Many programming environments include automated feedback in the form of hints to help novices learn autonomously. Some experimental studies investigated the impact of automated hints in the immediate performance and learning retention in that context. Automated feedback is also becoming a popular research topic in the context of formal specification languages, but so far no experimental studies have been conducted to assess its impact while learning such languages. Objective: We aim to investigate the impact of different types of automated hints while learning a formal specification language, not only in terms of immediate performance and learning retention, but also in the emotional response of the students. Method: We conducted a simple one-factor randomised experiment in 2 sessions involving 85 BSc students majoring in CSE. In the 1st session students were divided in 1 control group and 3 experimental groups, each receiving a different type of hint while learning to specify simple requirements with the Alloy formal specification language. To assess the impact of hints on learning retention, in the 2nd session, 1 week later, students had no hints while formalising requirements. Before and after each session the students answered a standard self-reporting emotional survey to assess their emotional response to the experiment. Results: Of the 3 types of hints considered, only those pointing to the precise location of an error had a positive impact on the immediate performance and none had significant impact in learning retention. Hint availability also causes a significant impact on the emotional response, but no significant emotional impact exists once hints are no longer available (i.e. no deprivation effects were detected). Conclusion: Although none of the evaluated hints had an impact on learning retention, learning a formal specification language with an environment that provides hints with precise error locations seems to contribute to a better overall experience without apparent drawbacks. Further studies are needed to investigate if other kind of feedback, namely hints combined with some sort of self-explanation prompts, can have a positive impact in learning retention. © 2024 Copyright held by the owner/author(s).

Facts & Figures

68Researchers

2016

1R&D Employees

2020

14Proceedings in indexed conferences

2020

Contacts