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2020 – today
- 2024
[c13]Márk Lajkó, Viktor Csuvik
, Tibor Gyimóthy, László Vidács:
Automated Program Repair with the GPT Family, Including GPT-2, GPT-3 and CodeX. APR 2024: 34-41- [c12]Viktor Csuvik, Dániel Horváth, László Vidács:
Feature Extraction, Learning and Selection in Support of Patch Correctness Assessment. ICSOFT 2024: 23-34 - [c11]Viktor Csuvik, Roland Aszmann, Árpád Beszédes, Ferenc Horváth, Tibor Gyimóthy:
On the Stability and Applicability of Deep Learning in Fault Localization. SANER 2024: 546-555 - 2023
- [c10]Dániel Horváth, Viktor Csuvik, Tibor Gyimóthy, László Vidács:
An Extensive Study on Model Architecture and Program Representation in the Domain of Learning-based Automated Program Repair. APR@ICSE 2023: 31-38 - [c9]Viktor Csuvik, Tibor Gyimóthy, László Vidács:
Can ChatGPT Fix My Code? ICSOFT 2023: 478-485 - 2022
- [c8]Márk Lajkó, Dániel Horváth, Viktor Csuvik, László Vidács:
Fine-Tuning GPT-2 to Patch Programs, Is It Worth It? ICCSA (Workshops 4) 2022: 79-91 - [c7]Márk Lajkó, Viktor Csuvik, László Vidács:
Towards JavaScript program repair with Generative Pre-trained Transformer (GPT-2). APR@ICSE 2022: 61-68 - [c6]Viktor Csuvik, László Vidács:
FixJS: A Dataset of Bug-fixing JavaScript Commits. MSR 2022: 712-716 - 2021
- [j2]András Kicsi, Viktor Csuvik, László Vidács:
Large Scale Evaluation of Natural Language Processing Based Test-to-Code Traceability Approaches. IEEE Access 9: 79089-79104 (2021) - [c5]Viktor Csuvik, Dániel Horváth, Márk Lajkó, László Vidács:
Exploring Plausible Patches Using Source Code Embeddings in JavaScript. APR@ICSE 2021: 11-18 - [i1]Viktor Csuvik, Dániel Horváth, Márk Lajkó, László Vidács:
Exploring Plausible Patches Using Source Code Embeddings in JavaScript. CoRR abs/2103.16846 (2021)
2010 – 2019
- 2019
- [j1]András Kicsi, Viktor Csuvik, László Vidács, Ferenc Horváth, Árpád Beszédes, Tibor Gyimóthy, Ferenc Kocsis:
Feature analysis using information retrieval, community detection and structural analysis methods in product line adoption. J. Syst. Softw. 155: 70-90 (2019) - [c4]Viktor Csuvik, András Kicsi, László Vidács:
Evaluation of Textual Similarity Techniques in Code Level Traceability. ICCSA (4) 2019: 529-543 - [c3]Viktor Csuvik, András Kicsi, László Vidács:
Source code level word embeddings in aiding semantic test-to-code traceability. SST@ICSE 2019: 29-36 - 2018
- [c2]András Kicsi, Viktor Csuvik, László Vidács, Árpád Beszédes, Tibor Gyimóthy:
Feature Level Complexity and Coupling Analysis in 4GL Systems. ICCSA (5) 2018: 438-453 - [c1]András Kicsi, László Vidács, Viktor Csuvik, Ferenc Horváth, Árpád Beszédes, Ferenc Kocsis:
Supporting Product Line Adoption by Combining Syntactic and Textual Feature Extraction. ICSR 2018: 148-163
Coauthor Index
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