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Kádár I, Hegedüs P, Ferenc R, Gyimóthy T.  2016.  Assessment of the Code Refactoring Dataset Regarding the Maintainability of Methods. Computational Science and Its Applications – ICCSA 2016: 16th International Conference, Beijing, China, July 4-7, 2016, Proceedings, Part IV. :610–624.

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.

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Faragó C, Hegedüs P, Végh ÁZoltán, Ferenc R.  2014.  Connection Between Version Control Operations and Quality Change of the Source Code. {Acta Cybernetica}. 21:585–607.

Faragó C, Hegedüs P, Végh ÁZoltán, Ferenc R.  2014.  Connection Between Version Control Operations and Quality Change of the Source Code. {Acta Cybernetica}. 21:585–607.

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Faragó C, Hegedüs P, Ferenc R.  2015.  Cumulative Code Churn: Impact on Maintainability. Proceedings of the 15th IEEE International Working Conference on Source Code Analysis and Manipulation (SCAM). :141-150.

Faragó C, Hegedüs P, Ferenc R.  2015.  Cumulative Code Churn: Impact on Maintainability. Proceedings of the 15th IEEE International Working Conference on Source Code Analysis and Manipulation (SCAM). :141-150.

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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.

Faragó C, Hegedüs P.  2018.  Developer Focus: Lack of Impact on Maintainability. Proceedings of the International Conference on Computational Science and Its Applications – ICCSA 2018. 10964:391–402.