3 papers
cs.SE2025
Identifying Helpful Context for LLM-based Vulnerability Repair: A Preliminary Study
Gábor Antal, Bence Bogenfürst, Rudolf Ferenc +1
Recent advancements in large language models (LLMs) have shown promise for automated vulnerability detection and repair in software systems. This paper investigates the performance…
cs.SE2025
Leveraging GPT-4 for Vulnerability-Witnessing Unit Test Generation
Gábor Antal, Dénes Bán, Martin Isztin +2
In the life-cycle of software development, testing plays a crucial role in quality assurance. Proper testing not only increases code coverage and prevents regressions but it can al…
cs.SE2024
Assessing GPT-4-Vision's Capabilities in UML-Based Code Generation
Gábor Antal, Richárd Vozár, Rudolf Ferenc
The emergence of advanced neural networks has opened up new ways in automated code generation from conceptual models, promising to enhance software development processes. This pape…