Publications

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Author Title Type [ Year(Desc)]
Filters: Author is László Vidács and Keyword is select:deep  [Clear All Filters]
2018
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).
2019
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, Horváth F, Beszédes Á, Gyimothy 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.
Csuvik V, Kicsi A, Vidács L.  2019.  Source Code Level Word Embeddings in Aiding Semantic Test-to-Code Traceability. Proceedings of the 10th International Workshop on Software and Systems Traceability, (SST 2019 @ ICSE). :29-36.
Tóth L, Nagy B, Janthó D, Vidács L, Gyimothy T.  2019.  Towards an Accurate Prediction of the Question Quality at Stack Overflow Using a Deep-Learning-Based NLP Approach. Proceedings of ICSOFT 2019, 14th International Conference on Software Technologies. :631-639.