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About

About

I am a Ph.D. in Computer Science in the MAP-i Doctoral Programme at the Universities of Minho, Aveiro and Porto. I hold a degree in Informatics and Computation Engineering from the Faculty of Engineering of the University of Porto. Member of the Software Engineering Group, FEUP. I teach as an Assistant Professor at FEUP/DEI. I'm also a member of the Hillside Group.

Interest
Topics
Details

Details

  • Name

    Hugo Sereno Ferreira
  • Cluster

    Computer Science
  • Role

    External Research Collaborator
  • Since

    01st January 2009
Publications

2018

A Brief Overview of Existing Tools for Testing the Internet-of-Things

Authors
Dias, JP; Couto, F; Paiva, ACR; Ferreira, HS;

Publication
2018 IEEE International Conference on Software Testing, Verification and Validation Workshops, ICST Workshops, Västerås, Sweden, April 9-13, 2018

Abstract
Systems are error-prone. Big systems have lots of errors. The Internet-of-Things poses us one of the biggest and widespread systems, where errors directly impact people's lives. Testing and validating is how one deals with errors; but testing and validating a planetary-scale, heterogeneous, and evergrowing ecosystem has its own challenges and idiosyncrasies. As of today, the solutions available for testing these systems are insufficient and fragmentary. In this paper we provide an overview on test approaches, tools and methodologies for the Internet-of-Things, its software and its devices. Our conclusion is that we are still lagging behind on the best practices and lessons learned from the Software Engineering community in the past decades. © 2018 IEEE.

2018

Dynamic Allocation of Serverless Functions in IoT Environments

Authors
Pinto, D; Dias, JP; Ferreira, HS;

Publication
CoRR

Abstract

2017

Automating the Extraction of Static Content and Dynamic Behaviour from e-Commerce Websites

Authors
Dias, JP; Ferreira, HS;

Publication
8TH INTERNATIONAL CONFERENCE ON AMBIENT SYSTEMS, NETWORKS AND TECHNOLOGIES (ANT-2017) AND THE 7TH INTERNATIONAL CONFERENCE ON SUSTAINABLE ENERGY INFORMATION TECHNOLOGY (SEIT 2017)

Abstract
E-commerce website owners rely heavily on analysing and summarising the behaviour of costumers, making efforts to influence user actions and optimize success metrics. Machine learning and data mining techniques have been applied in this field, greatly influencing the Internet marketing activities. When faced with a new e-commerce website, the data scientist starts a process of collecting real-time and historical data about it, analysing and transforming this data in order to get a grasp into the website and its users. Data scientists commonly resort to tracking domain-specific events, requiring code modification of the web pages. This paper proposes an alternative approach to retrieve information from a given e-commerce website, collecting data from the site's structure, retrieving semantic information in predefined locations and analysing user's access logs, thus enabling the development of accurate models for predicting users' future behaviour. This is accomplished by the application of a web mining process, comprehending the site's structure, content and usage in a pipeline, resulting in a web graph of the website, complemented with a categorization of each page and the website's archetypical user profiles. 1877-0509 (C) 2017 The Authors. Published by Elsevier B.V.

2017

Engineering Software for the Cloud: Messaging Systems and Logging

Authors
Sousa, TB; Ferreira, HS; Correia, FF; Aguiar, A;

Publication
Proceedings of the 22nd European Conference on Pattern Languages of Programs, EuroPLoP 2017, Irsee, Germany, July 12-16, 2017

Abstract

2017

Towards a framework for agent-based simulation of user behaviour in E-commerce context

Authors
Duarte, D; Ferreira, HS; Dias, JP; Kokkinogenis, Z;

Publication
Advances in Intelligent Systems and Computing

Abstract
In order to increase sales and profits, it is common that e-commerce website owners resort to several marketing and advertising techniques, attempting to influence user actions. Summarizing and analysing user behaviour is a complex task since it is hard to extrapolate patterns that never occurred before and the causality aspects of the system are not usually taken into consideration. There has been studies about characterizing user behaviour and interactions in e-commerce websites that could be used to improve this process. This paper presents an agent-based framework for simulating models of user behaviour created through data mining processes within an e-commerce context. The purpose of framework is to study the reaction of user to stimuli that influence their actions while navigating the website. Furthermore a scalability analysis is performed on a case-study. © Springer International Publishing AG 2018.