2 Search Results for "Freitas, Tiago Carvalho"

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)

Copy BibTex To Clipboard

@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.2023.9
NLP/AI Based Techniques for Programming Exercises Generation

Authors: Tiago Carvalho Freitas, Alvaro Costa Neto, Maria João Varanda Pereira, and Pedro Rangel Henriques

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

Abstract
This paper focuses on the enhancement of computer programming exercises generation to the benefit of both students and teachers. By exploring Natural Language Processing (NLP) and Machine Learning (ML) methods for automatic generation of text and source code, it is possible to semi-automatically construct programming exercises, aiding teachers to reduce redundant work and more easily apply active learning methodologies. This would not only allow them to still play a leading role in the teaching-learning process, but also provide students a better and more interactive learning experience. If embedded in a widely accessible website, an exercises generator with these Artificial Intelligence (AI) methods might be used directly by students, in order to obtain randomised lists of exercises for their own study, at their own time. The emergence of new and increasingly powerful technologies, such as the ones utilised by ChatGPT, raises the discussion about their use for exercise generation. Albeit highly capable, monetary and computational costs are still obstacles for wider adoption, as well as the possibility of incorrect results. This paper describes the characteristics and behaviour of several ML models applied and trained for text and code generation and their use to generate computer programming exercises. Finally, an analysis based on correctness and coherence of the resulting exercise statements and complementary source codes generated/produced is presented, and the role that this type of technology can play in a programming exercise automatic generation system is discussed.
Cite as

Tiago Carvalho Freitas, Alvaro Costa Neto, Maria João Varanda Pereira, and Pedro Rangel Henriques. NLP/AI Based Techniques for Programming Exercises Generation. In 4th International Computer Programming Education Conference (ICPEC 2023). Open Access Series in Informatics (OASIcs), Volume 112, pp. 9:1-9:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

Copy BibTex To Clipboard

@InProceedings{freitas_et_al:OASIcs.ICPEC.2023.9,
  author =	{Freitas, Tiago Carvalho and Costa Neto, Alvaro and Pereira, Maria Jo\~{a}o Varanda and Henriques, Pedro Rangel},
  title =	{{NLP/AI Based Techniques for Programming Exercises Generation}},
  booktitle =	{4th International Computer Programming Education Conference (ICPEC 2023)},
  pages =	{9:1--9:12},
  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.2023.9},
  URN =		{urn:nbn:de:0030-drops-185058},
  doi =		{10.4230/OASIcs.ICPEC.2023.9},
  annote =	{Keywords: Natural Language Processing, Computer Programming Education, Exercises Generation, Text Generation, Code Generation}
}
  • Refine by Type
  • 2 Document/PDF
  • 1 Document/HTML

  • Refine by Publication Year
  • 1 2025
  • 1 2023

  • Refine by Author
  • 1 Amburi, Sathwik
  • 1 Costa Neto, Alvaro
  • 1 Espinha Gasiba, Tiago
  • 1 Freitas, Tiago Carvalho
  • 1 Henriques, Pedro Rangel
  • Show More...

  • Refine by Series/Journal
  • 2 OASIcs

  • Refine by Classification
  • 1 Applied computing → Distance learning
  • 1 Applied computing → E-learning
  • 1 Applied computing → Learning management systems
  • 1 Computing methodologies → Machine learning
  • 1 Security and privacy → Software security engineering
  • Show More...

  • Refine by Keyword
  • 1 Code Generation
  • 1 Computer Programming Education
  • 1 Exercises Generation
  • 1 Generative AI
  • 1 Industry
  • Show More...

Any Issues?
X

Feedback on the Current Page

CAPTCHA

Thanks for your feedback!

Feedback submitted to Dagstuhl Publishing

Could not send message

Please try again later or send an E-mail
© 2023-2026 Schloss Dagstuhl – LZI GmbH Schloss Dagstuhl – LZI GmbH About DROPS Imprint Privacy Contact