From the 1 of 10 linked papers with an AI index.
10 papers
What Bugs Do Prolog Students Write? An Empirical Taxonomy and Data-Driven Mutation Framework
Ricardo Brancas, Pedro Orvalho, Carolina Carreira +2
Automated feedback tools for logic programming education depend on realistic bug datasets that reflect the mistakes students actually make. However, existing mutation testing frame…
MaxSAT-Based Feedback for Guiding Vision-Language Models in Sudoku
Pedro Orvalho, Guillem AlenyÃ, Felip ManyÃ
The paper introduces a neuro‑symbolic method that uses a MaxSAT solver to check and refine the outputs of vision‑language models when solving Sudoku, providing feedback that improv…
Reliable Reasoning with Large Language Models via Preference-Based Maximum Satisfiability
Pedro Orvalho, Marta Kwiatkowska, Guillem Alenyà +1
Large Language Models (LLMs) excel at understanding natural language but struggle with optimisation tasks involving multiple constraints and user-defined preferences, which commonl…
Are Large Language Models Robust in Understanding Code Against Semantics-Preserving Mutations?
Pedro Orvalho, Marta Kwiatkowska
With the widespread adoption of vibe coding, understanding the reasoning and robustness of Large Language Models (LLMs) is critical for their reliable use in programming tasks. Whi…
Model-Based Diagnosis with Multiple Observations: A Unified Approach for C Software and Boolean Circuits
Pedro Orvalho, Marta Kwiatkowska, Mikoláš Janota +1
Debugging is one of the most time-consuming and expensive tasks in software development and circuit design. Several formula-based fault localisation (FBFL) methods have been propos…
CFaults: Model-Based Diagnosis for Fault Localization in C Programs with Multiple Test Cases
Pedro Orvalho, Mikoláš Janota, Vasco Manquinho
Debugging is one of the most time-consuming and expensive tasks in software development. Several formula-based fault localization (FBFL) methods have been proposed, but they fail t…