The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Tibor Gyimóthy sits on this spectrum.
This scientist: 177 publications — 37th percentile
37% of scientists in this discipline score the same or lower.
The last bar groups every scientist with 991 publications or more.
The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Tibor Gyimóthy sits on this spectrum.
This scientist: 38 D-Index — 30th percentile
30% of scientists in this discipline score the same or lower.
The last bar groups every scientist with 131 D-Index or more.
Tibor Gyimóthy is affiliated with the University of Szeged in Hungary and has contributed extensively to the field of computer science, particularly in software engineering and software reliability research. Their work spans multiple subfields including information systems, software development, and signal processing.
Their recent publications reveal a focus on software testing, debugging techniques, and bug prediction methodologies. Notable papers include "A public unified bug dataset for java and its assessment regarding metrics and bug prediction" (2020, Software Quality Journal), "Deep learning in static, metric-based bug prediction" (2020, Array), and "Static JavaScript Call Graphs: A Comparative Study" (2024, arXiv (Cornell University)). Other relevant contributions are "Using contextual knowledge in interactive fault localization" (2022, Empirical Software Engineering) and "Unified Bug Dataset" (2020, Zenodo (CERN European Organization for Nuclear Research)).
Focus areas in the main topics of their research are:
Frequent co-authors collaborating with Tibor Gyimóthy include:
Their work appears regularly in publication venues such as:
Within computer science, Gyimóthy's research intersects multiple subfields:
The body of work demonstrates a sustained engagement with software engineering problems, data mining, and reliability assessment, contributing both empirical studies and methodologies relevant to fault localization and bug dataset development.
T. Gyimothy;R. Ferenc;I. Siket
Denys Poshyvanyk;Andrian Marcus;Rudolf Ferenc;Tibor Gyimóthy
R. Ferenc;A. Beszedes;M. Tarkiainen;T. Gyimothy
Tibor Gyimóthy;Árpád Beszédes;Istán Forgács
Peter Fritzson;Nahid Shahmehri;Mariam Kamkar;Tibor Gyimothy
Dóra Csendes;János Csirik;Tibor Gyimóthy;András Kocsor
Tibor Bakota;Peter Hegedus;Peter Kortvelyesi;Rudolf Ferenc
T. Bakota;R. Ferenc;T. Gyimothy
Árpád Beszédes;Rudolf Ferenc;Tibor Gyimóthy;André Dolenc
A. Beszedes;T. Gergely;Z. Mihaly Szabo;J. Csirik
Yixun Liu;Denys Poshyvanyk;Rudolf Ferenc;Tibor Gyimothy
R. Ferenc;I. Siket;T. Gyimothy
Dóra Csendes;János Csirik;Tibor Gyimóthy
Peter Fritzson;Tibor Gyimothy;Mariam Kamkar;Nahid Shahmehri
Dave Binkley;Sebastian Danicic;Tibor Gyimóthy;Mark Harman
R. Ferenc;S.E. Sim;R.C. Holt;R. Koschke
Attila Bicsák;Ákos Kiss;Rudolf Ferenc;Tibor Gyimóthy
Bela Ujhazi;Rudolf Ferenc;Denys Poshyvanyk;Tibor Gyimothy
A. Kiss;J. Jasz;G. Lehotai;T. Gyimothy
Dave Binkley;Sebastian Danicic;Tibor Gyimóthy;Mark Harman
Václav Rajlich;Tibor Gyimóthy
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