ai4se

AI for Software Engineering

My research investigates how artificial intelligence, and especially Large Language Models, can be applied to core software engineering tasks: code generation, translation, testing, verification, and trustworthiness assessment. A central thread is rigorous benchmarking: building the evaluation frameworks needed to measure what AI tools can and cannot do reliably in real engineering contexts. I also write opinion and perspective pieces on what the AI revolution means for software quality, human expertise, and professional responsibility.

Code Generation Code Translation Test Generation Vulnerability Detection Code Analysis Benchmarking Trustworthy AI

Opinion & Perspectives

Thoughts, analyses, and reflections on the impact of artificial intelligence on the software engineering profession.

Unpublished Opinion Piece

Teaching Software Engineering in the Age of Generative AI: What Is Really at Stake?

Marco Vieira
The arrival of generative AI in software development raises important questions: should we continue teaching programming as before, and to what extent will future engineers need to code? Before answering, it is essential to clarify a common confusion: programming, software development, and software engineering are not the same.
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Perspective  ·  IEEE Computer  ·  2025

Why We Should Trust Systems, Not Just Their AI/ML Components

Marco Vieira  ·  IEEE Computer, Vol. 58, No. 11, pp. 84–94, 2025
The hype around trustworthy AI primarily emphasizes fairness, robustness, and explainability of models but overlooks a wellknown reality: AI does not run in isolation! We call for a holistic perspective into trustworthy systems that considers the infrastructure, systemic interactions, governance, and humans in the loop.
  Read at IEEE Computer
Perspective  ·  IEEE Computer  ·  2025

Leveraging LLMs for Trustworthy Software Engineering: Insights and Challenges

Marco Vieira  ·  IEEE Computer, Vol. 58, No. 7, pp. 79–90, 2025
Large language models (LLMs) are transforming software engineering by accelerating development, reducing complexity, and cutting costs. If fully integrated into the software lifecycle they will have the potential to drive design, development, and deployment. However, LLM-driven trustworthy software engineering requires addressing multiple challenges.
  Read at IEEE Computer

Courses, Keynotes & Tutorials

University courses, presentations, and lectures on the intersection of generative AI and software engineering.

Course  ·  Spring 2026

AI-Driven Trustworthy Software Development

University of North Carolina at Charlotte  ·  (MSc, PhD)
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Keynote  ·  Nov 2025

Benchmarking GenAI for Software Engineering: Challenges and Insights

AISM @ ASE 2025  ·  Seoul, South Korea
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Keynote  ·  Nov 2024

LLMs for Trustworthy Software Engineering: Insights and Challenges

LADC 2024  ·  Recife, PE, Brazil
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Research Papers & Frameworks

Selected works on AI and ML applied to software engineering tasks. Full list available on the publications page.

Marco's RA (Online)
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