From Brittle LLM Code Reasoning to MaxSAT-Based Verified Repairs @ UCL
In this talk, we examine the limitations of Large Language Models (LLMs) in semantic code reasoning, showing that their predictions may change under semantics-preserving code …
Towards Assessing and Repairing LLM-Generated Code via Model Checking and MaxSAT-Based Fault Localisation @ Dagstuhl 2026
LLMs for code often lack true semantic understanding, evidenced by their instability under semantics-preserving transformations, and we address this by integrating formal methods …
PyVeritas: On Verifying Python via LLM-Based Transpilation and Bounded Model Checking for C @ P-AI-FM@AAAI 2026
In this talk I will present PyVeritas, a novel framework that leverages Large Language Models (LLMs) for high-level transpilation from Python to C, followed by bounded model …
MENTOR: Fixing Introductory Programming Assignments With Formula-Based Fault Localization and LLM-Driven Program Repair @ INESC-ID
In this talk, I will present my PhD work on MENTOR, a semantic automated program repair framework designed to provide meaningful feedback for introductory programming assignments.
From Logic to Learning: Rethinking Programming for the AI Era @ DEI, IST-UL
In this talk, I will present approaches that leverage the precision of formal logic and the adaptability of learning-based models to enable intelligent code generation, automated …
Counterexample Guided Program Repair Using Large Language Models and MaxSAT-based Fault Localization @ IIIA-CSIC
In this talk I will present a hybrid method to automated repair of C code, using Maximum Satisfiability (MaxSAT)-based fault localization, CFaults, to localize bugs and LLMs to …
Are Large Language Models Robust in Understanding Code Against Semantics-Preserving Mutations? @ Oxford
In this talk, I will present our evaluation on whether state-of-the-art LLMs with up to 8B parameters can reason about Python programs or are simply guessing.
MENTOR: Automated Feedback for Introductory Programming Exercises @ FCT-UNL
In my talk, I will present MENTOR, a semantic automated program repair (APR) framework designed to provide Automated Feedback for Introductory Programming Exercises.
MENTOR: Feedback Automático para Exercícios Introdutórios de Programação (Portuguese) @ VoA 2025
Nesta palestra, irei apresentar o sistema MENTOR, uma ferramenta de reparação automática de programas orientada para fornecer feedback automatizado para exercícios introdutórios de …
Counterexample Guided Program Repair Using Zero-Shot Learning and MaxSAT-based Fault Localization @ OutSystems
In this talk I will present our novel approach that combines the strengths of both FM-based fault localization and LLMs, via zero-shot learning, to enhance Automated Program …
GitSEED: A Git-backed Automated Assessment Tool for Software Engineering and Programming Education @ SIGCSE-Virtual 2024
In this talk I will present GitSEED, a language-agnostic automated assessment tool designed for Programming Education and Software Engineering (SE) and backed by GitLab.
LLM-Driven Automated Program Repair Using MaxSAT-based Fault Localization @ Oxford
In this talk I will present a hybrid method to automated repair of C code, using Maximum Satisfiability (MaxSAT)-based fault localization, CFaults, to localize bugs and LLMs to …
CFaults: Model-Based Diagnosis for Fault Localization in C with Multiple Test Cases @ FM 2024
In this talk I will introduce a novel fault localization approach for C programs with multiple faults. CFaults leverages Model-Based Diagnosis (MBD) with multiple observations and …
C-Pack of IPAs: A C90 Program Benchmark of Introductory Programming Assignments @ APR@ICSE 2024
In this talk I will present C-Pack-IPAs, a publicly available benchmark comprising student-program submissions for 25 distinct introductory programming assignments (IPAs).
UpMax: User partitioning for MaxSAT @ SAT 2023
In this talk I will present a new framework called UpMax that decouples the partitioning procedure from the MaxSAT solving algorithms.
Learning Variable Mappings to Repair and Verify Programs @ CIIRC 2023
In this talk I will propose using graph neural networks (GNNs) to map the set of variables between two programs based on both programs' abstract syntax trees (ASTs).