Hegedüs P, Kádár I, Ferenc R, Gyimóthy T.  2018.  Empirical evaluation of software maintainability based on a manually validated refactoring dataset. Information and Software Technology. 95:313-327.

Bán D, Ferenc R, Siket I, Kiss Á, Gyimóthy T.  2018.  Prediction Models for Performance, Power, and Energy Efficiency of Software Executed on Heterogeneous Hardware. Journal of Supercomputing. :25.

Zsolt S, Alex S-N, István NT, Ádám C-K, Vincze V, Farkas R.  2018.  Relevance Segmentation of Long Documents. XIV. Magyar Számítógépes Nyelvészeti Konferencia. :405-412.

Antal G, Hegedüs P, Tóth Z, Ferenc R, Gyimóthy T.  2018.  Static JavaScript Call Graphs: A Comparative Study. SCAM. :177-186.

Ferenc R, Hegedűs P, Gyimesi P, Antal G, Bán D, Gyimóthy T.  2019.  Challenging Machine Learning Algorithms in Predicting Vulnerable JavaScript Functions. Proceedings of the 7th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering. :8–14.

Ban D, Ferenc R, Siket I, Kiss A, Gyimothy T.  2019.  Prediction models for performance, power, and energy efficiency of software executed on heterogeneous hardware. JOURNAL OF SUPERCOMPUTING. 75:4001-4025.

Ferenc R, Viszkok T, Aladics T, Jász J, Hegedűs P.  2020.  Deep-water framework: The Swiss army knife of humans working with machine learning models. SoftwareX. 12:100551.