7 Search Results for "Teixeira Pinto, Mário Paulo"

Volume

OASIcs, Volume 112

4th International Computer Programming Education Conference (ICPEC 2023)

ICPEC 2023, June 26-28, 2023, Vila do Conde, Portugal

Editors: Ricardo Alexandre Peixoto de Queirós and Mário Paulo Teixeira Pinto

Document
DOI: 10.4230/OASIcs.ICPEC.2025.2
Enabling Secure Coding: Exploring GenAI for Developer Training and Education

Authors: Sathwik Amburi, Tiago Espinha Gasiba, Ulrike Lechner, and Maria Pinto-Albuquerque

Published in: OASIcs, Volume 133, 6th International Computer Programming Education Conference (ICPEC 2025)

Abstract
The rapid adoption of GenAI for code generation presents unprecedented opportunities and significant security challenges. Raising awareness about secure coding is critical for preventing software vulnerabilities. To investigate how Generative AI can best support secure coding, we built an AI Secure Coding platform, an interactive training environment that embeds a GPT-4 based chatbot directly into a structured challenge workflow. The platform comprises a landing page, a challenges page with three AI-generated tasks, and a challenge page where participants work with code snippets. In each challenge, developers (1) identify vulnerabilities by reviewing code and adding comments, (2) ask the AI for help via a chat based interface, (3) review and refine comments based on AI feedback, and (4) fix vulnerabilities by submitting secure patches. The study involved 18 industry developers tackling three challenges. Participants used the AI Secure Coding Platform to detect and remediate vulnerabilities and then completed a survey to capture their opinions and comfort level with AI assisted platform for secure coding. Results show that AI assistance can boost productivity, reduce errors, and uncover more defects when treated as a "second pair of eyes," but it can also foster over-reliance. This study introduces the AI Secure Coding platform, presents preliminary results from a initial study, and shows that embedding GenAI into a structured secure-coding workflow can both enable and challenge developers. This work also opens the door to a new research field: leveraging GenAI to enable secure software development.
Cite as

Sathwik Amburi, Tiago Espinha Gasiba, Ulrike Lechner, and Maria Pinto-Albuquerque. Enabling Secure Coding: Exploring GenAI for Developer Training and Education. In 6th International Computer Programming Education Conference (ICPEC 2025). Open Access Series in Informatics (OASIcs), Volume 133, pp. 2:1-2:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{amburi_et_al:OASIcs.ICPEC.2025.2,
  author =	{Amburi, Sathwik and Espinha Gasiba, Tiago and Lechner, Ulrike and Pinto-Albuquerque, Maria},
  title =	{{Enabling Secure Coding: Exploring GenAI for Developer Training and Education}},
  booktitle =	{6th International Computer Programming Education Conference (ICPEC 2025)},
  pages =	{2:1--2:15},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-393-5},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{133},
  editor =	{Queir\'{o}s, Ricardo and Pinto, M\'{a}rio and Portela, Filipe and Sim\~{o}es, Alberto},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICPEC.2025.2},
  URN =		{urn:nbn:de:0030-drops-240321},
  doi =		{10.4230/OASIcs.ICPEC.2025.2},
  annote =	{Keywords: Secure Coding, Industry, Software Development, Generative AI, Large Language Models, Teaching}
}
Document
DOI: 10.4230/OASIcs.ICPEC.2025.4
Can Open Large Language Models Catch Vulnerabilities?

Authors: Diogo Gaspar Lopes, Tiago Espinha Gasiba, Sathwik Amburi, and Maria Pinto-Albuquerque

Published in: OASIcs, Volume 133, 6th International Computer Programming Education Conference (ICPEC 2025)

Abstract
As Large Language Models (LLMs) become increasingly integrated into secure software development workflows, a critical question remains unanswered: can these models not only detect insecure code but also reliably classify vulnerabilities according to standardized taxonomies? In this work, we conduct a systematic evaluation of three state-of-the-art LLMs - Llama3, Codestral, and Deepseek R1 - using a carefully filtered subset of the Big-Vul dataset annotated with eight representative Common Weakness Enumeration categories. Adopting a closed-world classification setup, we assess each model’s performance in both identifying the presence of vulnerabilities and mapping them to the correct CWE label. Our findings reveal a sharp contrast between high detection rates and markedly poor classification accuracy, with frequent overgeneralization and misclassification. Moreover, we analyze model-specific biases and common failure modes, shedding light on the limitations of current LLMs in performing fine-grained security reasoning.These insights are especially relevant in educational contexts, where LLMs are being adopted as learning aids despite their limitations. A nuanced understanding of their behaviour is essential to prevent the propagation of misconceptions among students. Our results expose key challenges that must be addressed before LLMs can be reliably deployed in security-sensitive environments.
Cite as

Diogo Gaspar Lopes, Tiago Espinha Gasiba, Sathwik Amburi, and Maria Pinto-Albuquerque. Can Open Large Language Models Catch Vulnerabilities?. In 6th International Computer Programming Education Conference (ICPEC 2025). Open Access Series in Informatics (OASIcs), Volume 133, pp. 4:1-4:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{gasparlopes_et_al:OASIcs.ICPEC.2025.4,
  author =	{Gaspar Lopes, Diogo and Espinha Gasiba, Tiago and Amburi, Sathwik and Pinto-Albuquerque, Maria},
  title =	{{Can Open Large Language Models Catch Vulnerabilities?}},
  booktitle =	{6th International Computer Programming Education Conference (ICPEC 2025)},
  pages =	{4:1--4:14},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-393-5},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{133},
  editor =	{Queir\'{o}s, Ricardo and Pinto, M\'{a}rio and Portela, Filipe and Sim\~{o}es, Alberto},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICPEC.2025.4},
  URN =		{urn:nbn:de:0030-drops-240340},
  doi =		{10.4230/OASIcs.ICPEC.2025.4},
  annote =	{Keywords: Large Language Models (LLMs), Secure Coding, CWE Classification, Machine Learning, Software Vulnerability Detection, Artificial Intelligence, Code Analysis, Big-Vul Dataset}
}
Document
Complete Volume
DOI: 10.4230/OASIcs.ICPEC.2023
OASIcs, Volume 112, ICPEC 2023, Complete Volume

Authors: Ricardo Alexandre Peixoto de Queirós and Mário Paulo Teixeira Pinto

Published in: OASIcs, Volume 112, 4th International Computer Programming Education Conference (ICPEC 2023)

Abstract
OASIcs, Volume 112, ICPEC 2023, Complete Volume
Cite as

4th International Computer Programming Education Conference (ICPEC 2023). Open Access Series in Informatics (OASIcs), Volume 112, pp. 1-152, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@Proceedings{peixotodequeiros_et_al:OASIcs.ICPEC.2023,
  title =	{{OASIcs, Volume 112, ICPEC 2023, Complete Volume}},
  booktitle =	{4th International Computer Programming Education Conference (ICPEC 2023)},
  pages =	{1--152},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-290-7},
  ISSN =	{2190-6807},
  year =	{2023},
  volume =	{112},
  editor =	{Peixoto de Queir\'{o}s, Ricardo Alexandre and Teixeira Pinto, M\'{a}rio Paulo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICPEC.2023},
  URN =		{urn:nbn:de:0030-drops-184951},
  doi =		{10.4230/OASIcs.ICPEC.2023},
  annote =	{Keywords: OASIcs, Volume 112, ICPEC 2023, Complete Volume}
}
Document
Front Matter
DOI: 10.4230/OASIcs.ICPEC.2023.0
Front Matter, Table of Contents, Preface, Conference Organization

Authors: Ricardo Alexandre Peixoto de Queirós and Mário Paulo Teixeira Pinto

Published in: OASIcs, Volume 112, 4th International Computer Programming Education Conference (ICPEC 2023)

Abstract
Front Matter, Table of Contents, Preface, Conference Organization
Cite as

4th International Computer Programming Education Conference (ICPEC 2023). Open Access Series in Informatics (OASIcs), Volume 112, pp. 0:i-0:xii, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{peixotodequeiros_et_al:OASIcs.ICPEC.2023.0,
  author =	{Peixoto de Queir\'{o}s, Ricardo Alexandre and Teixeira Pinto, M\'{a}rio Paulo},
  title =	{{Front Matter, Table of Contents, Preface, Conference Organization}},
  booktitle =	{4th International Computer Programming Education Conference (ICPEC 2023)},
  pages =	{0:i--0:xii},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-290-7},
  ISSN =	{2190-6807},
  year =	{2023},
  volume =	{112},
  editor =	{Peixoto de Queir\'{o}s, Ricardo Alexandre and Teixeira Pinto, M\'{a}rio Paulo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICPEC.2023.0},
  URN =		{urn:nbn:de:0030-drops-184969},
  doi =		{10.4230/OASIcs.ICPEC.2023.0},
  annote =	{Keywords: Front Matter, Table of Contents, Preface, Conference Organization}
}
Document
Short Paper
DOI: 10.4230/OASIcs.ICPEC.2023.6
A Gamified Educational Escape Rooms' Framework for Computer Programming Classes (Short Paper)

Authors: Ricardo Queirós, Carla Pinto, Mário Cruz, and Daniela Mascarenhas

Published in: OASIcs, Volume 112, 4th International Computer Programming Education Conference (ICPEC 2023)

Abstract
This paper presents a study on the use of gamified educational escape rooms to foster the teaching-learning process of computer programming, based on an user type taxonomy. The ultimate goal of this work is to identify and validate the most suitable gamification elements and mechanics for each user profile, providing case studies that illustrate their implementation. The main contribution of this work is to guide the design process of educational escape rooms in any domain, by considering the needs, preferences, and motivations of different user types.
Cite as

Ricardo Queirós, Carla Pinto, Mário Cruz, and Daniela Mascarenhas. A Gamified Educational Escape Rooms' Framework for Computer Programming Classes (Short Paper). In 4th International Computer Programming Education Conference (ICPEC 2023). Open Access Series in Informatics (OASIcs), Volume 112, pp. 6:1-6:8, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{queiros_et_al:OASIcs.ICPEC.2023.6,
  author =	{Queir\'{o}s, Ricardo and Pinto, Carla and Cruz, M\'{a}rio and Mascarenhas, Daniela},
  title =	{{A Gamified Educational Escape Rooms' Framework for Computer Programming Classes}},
  booktitle =	{4th International Computer Programming Education Conference (ICPEC 2023)},
  pages =	{6:1--6:8},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-290-7},
  ISSN =	{2190-6807},
  year =	{2023},
  volume =	{112},
  editor =	{Peixoto de Queir\'{o}s, Ricardo Alexandre and Teixeira Pinto, M\'{a}rio Paulo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICPEC.2023.6},
  URN =		{urn:nbn:de:0030-drops-185021},
  doi =		{10.4230/OASIcs.ICPEC.2023.6},
  annote =	{Keywords: Escape Room, Gamification, Computer Programming, User Type Taxonomy}
}
Document
DOI: 10.4230/OASIcs.SLATE.2017.20
Natural Transmission of Information Extraction Results to End-Users - A Proof-of-Concept Using Data-to-Text

Authors: José Casimiro Pereira, António J. S. Teixeira, Mário Rodrigues, Pedro Miguel, and Joaquim Sousa Pinto

Published in: OASIcs, Volume 56, 6th Symposium on Languages, Applications and Technologies (SLATE 2017)

Abstract
Information Extraction from natural texts has a great potential in areas such as Tourism and can be of great assistance in transforming customers' comments in valuable information for Tourism operators, governments and customers. After extraction, information needs to be efficiently transmitted to end-users in a natural way. Systems should not, in general, send extracted information directly to end-users, such as hotel managers, as it can be difficult to read. Naturally, humans transmit and encode information using natural languages, such as Portuguese. The problem arising from the need of efficient and natural transmission of the information to end-user is how to encode it. The use of natural language generation (NLG) is a possible solution, for producing sentences, and, with them, texts. In this paper we address this, with a data-to-text system, a derivation of formal NLG systems that use data as input. The proposed system uses an aligned corpus, which was defined, collected and processed, in about approximately 3 weeks of work. To build the language model were used three different in-domain and out-of-domain corpora. The effects of this approach were evaluated, and results are presented. Automatic metrics, BLEU and Meteor, were used to evaluate the different systems, comparing their values with similar systems. Results show that expanding the corpus has a major positive effect in BLEU and Meteor scores and use of additional corpora (in-domain and out-of-domain) in training language model does not result in significantly different performance. The scores obtained, combined with their comparison with other systems performance and informal evaluation by humans of the sentences produced, give additional support for the capabilities of the translation based approach for fast development of data-to-text for new domains.
Cite as

José Casimiro Pereira, António J. S. Teixeira, Mário Rodrigues, Pedro Miguel, and Joaquim Sousa Pinto. Natural Transmission of Information Extraction Results to End-Users - A Proof-of-Concept Using Data-to-Text. In 6th Symposium on Languages, Applications and Technologies (SLATE 2017). Open Access Series in Informatics (OASIcs), Volume 56, pp. 20:1-20:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)

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@InProceedings{pereira_et_al:OASIcs.SLATE.2017.20,
  author =	{Pereira, Jos\'{e} Casimiro and Teixeira, Ant\'{o}nio J. S. and Rodrigues, M\'{a}rio and Miguel, Pedro and Pinto, Joaquim Sousa},
  title =	{{Natural Transmission of Information Extraction Results to End-Users - A Proof-of-Concept Using Data-to-Text}},
  booktitle =	{6th Symposium on Languages, Applications and Technologies (SLATE 2017)},
  pages =	{20:1--20:14},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-056-9},
  ISSN =	{2190-6807},
  year =	{2017},
  volume =	{56},
  editor =	{Queir\'{o}s, Ricardo and Pinto, M\'{a}rio and Sim\~{o}es, Alberto and Leal, Jos\'{e} Paulo and Varanda, Maria Jo\~{a}o},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.SLATE.2017.20},
  URN =		{urn:nbn:de:0030-drops-79530},
  doi =		{10.4230/OASIcs.SLATE.2017.20},
  annote =	{Keywords: Data-to-Text, Natural Language Generation, Automatic Translation, opinions, Tourism, Portuguese}
}
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