In this talk, we will present a framework for solving MaxSAT problems expressed in natural language by combining large language models with the PySAT toolkit. We will show how LLMs ā¦
Excited to share that two of our papers have been accepted at EPIA 2026, covering the robustness of LLMs for code understanding and neuro-symbolic feedback for Vision-Language ā¦
In this paper we assess whether SOTA LLMs can reason about Python programs or are simply guessing. We apply five semantics-preserving code mutations, which maintain program ā¦
Excited to share that three of our papers have been accepted at FLoC 2026, covering automated feedback for Prolog education, data-driven mutation testing for Prolog, and ā¦
In this paper, we study a neuro-symbolic approach in which an LLM translates a natural language description of an optimisation problem into executable Python code using PySAT. The ā¦
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 ā¦
LLMs for code often lack true semantic understanding, evidenced by their instability under semantics-preserving transformations, and we address this by integrating formal methods ā¦
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 ā¦
In this paper, we propose PyVeritas, a novel framework that leverages Large Language Models (LLMs) for high-level transpilation from Python to C, followed by bounded model checking ā¦
Iām excited to share that our paper on PyVeritas has been accepted to the P-AI-FM-26 Workshop @ AAAI 2026! š
This PhD thesis presents MENTOR, a semantic automated program repair (APR) framework designed to provide Automated Feedback for Introductory Programming Exercises.
In this paper, we propose a novel approach that combines the strengths of both FM-based fault localization and LLMs, via zero-shot learning, to enhance APR for IPAs. Our method ā¦
Localising system faults has long been recognised as one of the most time-consuming and costly tasks in software engineering. Given a buggy system, fault localisation (FL) refers ā¦
I am very happy to share that our paper that combines the strengths of both MaxSAT-based fault localisation and Large Language Models, via zero-shot learning, to enhance automated ā¦
Delivering valuable and personalised feedback to students remains one of the greatest challenges in programming education, particularly in courses with large enrollments. Providing ā¦
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 ā¦