Kicsi A, Tóth L, Vidács L.  2018.  Exploring the Benefits of Utilizing Conceptual Information in Test-to-Code Traceability. Proceedings of the IEEE/ACM 6th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE 2018 @ ICSE).

Kicsi A, Csuvik V, Vidács L, Horváth F, Beszédes Á, Gyimóthy T, Kocsis F.  2019.  Feature Analysis using Information Retrieval, Community Detection and Structural Analysis Methods in Product Line Adoption. Journal of Systems and Software. 155:70-90.

Kicsi A, Vidács L, Csuvik V, Horváth F, Beszédes Á, Kocsis F.  2018.  Supporting Product Line Adoption by Combining Syntactic and Textual Feature Extraction. New Opportunities for Software Reuse - 17th International Conference on Software Reuse (ICSR 2018). :1-16.

Kicsi A, Rákóczi M, Vidács L.  2019.  Exploration and Mining of Source Code Level Traceability Links on Stack Overflow. Proceedings of ICSOFT 2019, 14th International Conference on Software Technologies. :339-346.

Kicsi A, Csuvik V, Vidács L, Beszédes Á, Gyimóthy T.  2018.  Feature Level Complexity and Coupling Analysis in 4GL Systems. Proceedings of the 18th International Conference on Computational Science and Its Applications (ICCSA 2018).

Kicsi A, Vidács L, Gyimothy T.  2020.  TestRoutes: A Manually Curated Method Level Dataset for Test-to-Code Traceability. Proceedings of the 17th International Conference on Mining Software Repositories, MSR 2020. :593-597.

Kicsi A, Tóth L, Vidács L.  2018.  Exploring the Benefits of Utilizing Conceptual Information in Test-to-Code Traceability. Proceedings of the IEEE/ACM 6th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE 2018 @ ICSE).

Kertész-Farkas A, Fülöp Z, Kocsor A.  2003.  Magyar nyelvû szótárak tömör reprezentációja nemdeterminisztikus automatákkal. I. Magyar Számítógépes Nyelvészet Konferencia. :231-237.

Kalmár Z, Szepesvári C, Lörincz A.  1998.  Module-Based Reinforcement Learning: Experiments with a Real Robot. Machine Learning. 31:55–85.

Kálmán M., Havasi F., Gyimóthy T.  2003.  Compacting XML Documents. SPLST. :137-151.

Kádár I, Hegedüs P, Ferenc R, Gyimóthy T.  2016.  A Code Refactoring Dataset and Its Assessment Regarding Software Maintainability. Proceedings of the 23rd IEEE International Conference on Software Analysis, Evolution, and Reengineering (SANER). 1:599-603.

Kádár I, Hegedüs P, Ferenc R.  2015.  Adding Constraint Building Mechanisms to a Symbolic Execution Engine Developed for Detecting Runtime Errors. Proceedings of the International Conference on Computational Science and Its Applications – ICCSA 2015. 9159:20-35.

Kádár I, Hegedüs P, Ferenc R, Gyimóthy T.  2016.  A Manually Validated Code Refactoring Dataset and Its Assessment Regarding Software Maintainability. Proceedings of the 12th International Conference on Predictive Models and Data Analytics in Software Engineering. :10:1–10:4.

Kádár I, Hegedüs P, Ferenc R.  2014.  Runtime Exception Detection in Java Programs Using Symbolic Execution. Acta Cybernetica. 21:331–352.

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.

Jiang X., Abegglen K., Bunke H., Csirik J.  2003.  Dynamic computation of generalised median strings. Pattern Analysis & Applications. 6:185–193.

Jiang X., Abegglen K., Bunke H., Csirik J.  2003.  Dynamic Computation of Generalised Median Strings. Pattern Anal. Appl.. 6:185–193.

Jiang X, Bunke H, Csirik J.  2004.  MEDIAN STRINGS: A REVIEW. Data Mining in Time Series Databases. :173-192.

Jenei B, Berend G, Varga L.  2019.  Proactive Minimization of Convolutional Networks. 2019 International Joint Conference on Neural Networks (IJCNN). :1-7.

Jelasity M, Preuß M.  2002.  On Obtaining Global Information in a Peer-to-Peer Fully Distributed Environment (Research Note). Euro-Par. 2400:573–577.

Jelasity M.  1998.  UEGO, an Abstract Niching Technique for Global Optimization. PPSN. 1498:378–387.

Jelasity M, Ortigosa PMartínez, García I.  2001.  UEGO, an Abstract Clustering Technique for Multimodal Global Optimization. Journal of Heuristics. 7:215–233.

Jelasity M.  2001.  The shape of evolutionary search : discovering and representing search space structure.

Jelasity M., PreusB M., Van Steen M., Paechter B..  2002.  Maintaining Connectivity in a Scalable and Robust Distributed Environment. 2nd IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID'02). :389-394.

Jelasity M., Dombi J..  1995.  Gas, an approach to a solution of the niche radius problem. IEE Conference Publication. :424–429.