Development of a Novel Semantic-Based System Integration Framework. - András Bánhalmi; Dénes Paczolay; Ádám Zoltán Végh; Gábor Antal; Vilmos Bilicki (ECBS-EERC, 2013)

Bulk Fixing Coding Issues and Its Effects on Software Quality: Is It Worth Refactoring? - Gábor Szoke; Gabor Antal; Csaba Nagy 001; Rudolf Ferenc; Tibor Gyimóthy (SCAM, 2014)

Identifying wasted effort in the field via developer interaction data. - Gergö Balogh; Gabor Antal; Árpád Beszédes; László Vidács; Tibor Gyimóthy; Ádám Zoltán Végh (ICSME, 2015)

Transforming C++11 Code to C++03 to Support Legacy Compilation Environments. - Gabor Antal; David Havas; István Siket; Árpád Beszédes; Rudolf Ferenc; József Mihalicza (SCAM, 2016)

Empirical study on refactoring large-scale industrial systems and its effects on maintainability. - Gábor Szoke; Gabor Antal; Csaba Nagy 001; Rudolf Ferenc; Tibor Gyimóthy (J. Syst. Softw., 2017)

A Hands-on OpenStack Code Refactoring Experience Report. - Gábor Antal; Alex Szarka; Péter Hegedüs (ICCSA (5), 2018)

[Research Paper] Static JavaScript Call Graphs: A Comparative Study. - Gabor Antal; Péter Hegedüs; Zoltán Tóth; Rudolf Ferenc; Tibor Gyimóthy (SCAM, 2018)

Challenging machine learning algorithms in predicting vulnerable JavaScript functions. - Rudolf Ferenc; Péter Hegedüs; Péter Gyimesi; Gábor Antal; Dénes Bán; Tibor Gyimóthy (RAISE@ICSE, 2019)

A Data-Mining Based Study of Security Vulnerability Types and Their Mitigation in Different Languages. - Gábor Antal; Balázs Mosolygó; Norbert Vándor; Péter Hegedüs (ICCSA (4), 2020)

Exploring the Security Awareness of the Python and JavaScript Open Source Communities. - Gábor Antal; Márton Keleti; Péter Hegedüs (MSR, 2020)

On the Rise and Fall of Simple Stupid Bugs: a Life-Cycle Analysis of SStuBs. - Balázs Mosolygó; Norbert Vándor; Gábor Antal; Péter Hegedüs (MSR, 2021)

Don't DIY: Automatically transform legacy Python code to support structural pattern matching. - Balázs Rózsa; Gábor Antal; Rudolf Ferenc (SCAM, 2022)

Applying Code Analysis and Machine Learning Techniques to Improve Compatibility and Security of Programs - Gábor Antal (2022)

Is JavaScript Call Graph Extraction Solved Yet? A Comparative Study of Static and Dynamic Tools. - Gábor Antal; Péter Hegedüs; Zoltán Herczeg; Gábor Lóki; Rudolf Ferenc (IEEE Access, 2023)

Reality Check: Assessing GPT-4 in Fixing Real-World Software Vulnerabilities. - Zoltán Ságodi; Gábor Antal; Bence Bogenfürst; Martin Isztin; Péter Hegedüs; Rudolf Ferenc (EASE, 2024)

Toward a New Era of Rapid Development: Assessing GPT-4-Vision's Capabilities in UML-Based Code Generation. - Gábor Antal; Richárd Vozár; Rudolf Ferenc (LLM4CODE@ICSE, 2024)

On the Usefulness of Python Structural Pattern Matching: An Empirical Study. - Norbert Vándor; Gábor Antal; Péter Hegedüs; Rudolf Ferenc (SANER, 2024)

PyBugHive: A Comprehensive Database of Manually Validated, Reproducible Python Bugs. - Gábor Antal; Norbert Vándor; István Kolláth; Balázs Mosolygó; Péter Hegedüs; Rudolf Ferenc (IEEE Access, 2024)

Assessing GPT-4-Vision's Capabilities in UML-Based Code Generation. - Gábor Antal; Richárd Vozár; Rudolf Ferenc (CoRR, 2024)

Static JavaScript Call Graphs: A Comparative Study. - Gábor Antal; Péter Hegedüs; Zoltán Tóth; Rudolf Ferenc; Tibor Gyimóthy (CoRR, 2024)

Enhanced Bug Prediction in JavaScript Programs with Hybrid Call-Graph Based Invocation Metrics. - Gábor Antal; Zoltán Tóth; Péter Hegedüs; Rudolf Ferenc (CoRR, 2024)

Leveraging GPT-4 for Vulnerability-Witnessing Unit Test Generation. - Gábor Antal; Dénes Bán; Martin Isztin; Rudolf Ferenc; Péter Hegedüs (CoRR, 2025)

Identifying Helpful Context for LLM-based Vulnerability Repair: A Preliminary Study. - Gábor Antal; Bence Bogenfürst; Rudolf Ferenc; Péter Hegedüs (CoRR, 2025)