MENTOR: Automated Feedback for Introductory Programming Exercises
This PhD thesis presents MENTOR, a semantic automated program repair (APR) framework designed to provide Automated Feedback for Introductory Programming Exercises.
Counterexample Guided Program Repair Using Zero-Shot Learning and MaxSAT-based Fault Localization
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 …
Model-Based Diagnosis for Software
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 …
Artificial Intelligence for CS Education
Empowering the next generation of programmers requires intelligent systems that not only evaluate code but also teach, guide, and inspire. This research explores how Artificial …
📄 Paper accepted @ AAAI 2025!! 🎉
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 …
Automated Feedback for Introductory Programming Exercises
Delivering valuable and personalised feedback to students remains one of the greatest challenges in programming education, particularly in courses with large enrollments. Providing …
CFaults: Model-Based Diagnosis for Fault Localization in C with Multiple Test Cases
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 …
CFaults: Model-Based Diagnosis for Fault Localization in C with Multiple Test Cases
This paper introduces a novel fault localization approach for C programs with multiple faults. CFaults leverages Model-Based Diagnosis (MBD) with multiple observations and …
Graph Neural Networks For Mapping Variables Between Programs
In this work, we propose using graph neural networks (GNNs) to map the set of variables between two programs based on both programs' abstract syntax trees (ASTs). To demonstrate …
User partitioning for Maximum Satisfiability
The Maximum Satisfiability (MaxSAT) problem is the optimisation variant of the Satisfiability (SAT) problem. Solving MaxSAT efficiently often involves partitioning the set of soft …
UpMax: User partitioning for MaxSAT
In this talk I will present a new framework called UpMax that decouples the partitioning procedure from the MaxSAT solving algorithms.
UpMax: User partitioning for MaxSAT
This paper proposes a new framework called UpMax that decouples the partitioning procedure from the MaxSAT solving algorithms. As a result, new partitioning procedures can be …
Learning Variable Mappings to Repair and Verify Programs
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).
Project Proposal: Learning Variable Mappings to Repair Programs
In this talk I propose to learn how to map the set of variables between different small imperative programs based on both programs' abstract syntax trees (ASTs) using graph neural …
Project Proposal: Learning Variable Mappings to Repair Programs
In this position paper, we propose to learn how to map the set of variables between different small imperative programs based on both programs' abstract syntax trees (ASTs) using …