Project Publications
2023
| George Ciubotariu, Gabriela Czibula, Istvan Gergely Czibula, Ioana-Gabriela Chelaru – “Uncovering Behavioural Patterns of One: And Binary-Class SVM-Based Software Defect Predictors“, In Proceedings of the 18th International Conference on Software Technologies – ICSOFT, pp. 249-257 (B-ranked, indexed WoS) |
| Anamaria Briciu, Gabriela Czibula, Mihaiela Lupea – “A study on the relevance of semantic features extracted using BERT-based language models for enhancing the performance of software defect classifiers”, 27th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES2023), accepted for publication (B-ranked, indexed WoS) |
| Gabriela Czibula, Ioana-Gabriela Chelaru, Istvan Gergely Czibula, Arthur Molnar – “An unsupervised learning-based methodology for uncovering behavioural patterns for specific types of software defects“, 27th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES2023), accepted for publication (B-ranked, indexed WoS) |
| Zsuzsanna Marian-Oneț, Diana-Lucia Miholca – “Source-code embedding-based software defect prediction“, In Proceedings of the 18th International Conference on Software Technologies – ICSOFT, pp. 185-196 (B-ranked, indexed WoS) |
| Mariana Maier, Gabriela Czibula, Lavinia Delean – “Using unsupervised learning for mining behavioural patterns from data. A case study for the baccalaureate exam in Romania“, Studies in Informatics and Control, vol. 32(2), pp. 73-84, 2023 (2022 IF=1.6, Q3) |
| Imre-Gergely Mali, Gabriela Czibula – “Policy-Based Reinforcement Learning in the Generalized Rock-Paper-Scissors Game, ESANN 2023 proceedings“, The 31th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2023), pp. 345-350 (B-ranked, indexed WoS) |
| Alexandra-Ioana Albu – “Temporal ensembling-based deep k-nearest neighbours for learning with noisy labels“, The 31th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2023), pp. 483-488 (B-ranked, indexed WoS) |
| Paul-Dumitru Orășan, Gabriela Czibula – “Im2Vide0: A Zero-Shot approach using diffusion models for natural language conditioned Image-to-Video“, 2023 IEEE 19th International Conference on Intelligent Computer Communication and Processing, 2023, accepted for publication (D-ranked, indexed IEEE) |
2022
| Diana-Lucia Miholca, Vlad-Ioan Tomescu, Gabriela Czibula – “An in-depth analysis of the software features’ impact on the performance of deep learning-based software defect predictors“, IEEE Access, 2022, Volume 10, pp. 64801-64818 (B-ranked, indexed WoS, 2021 IF=3.476, Q2) |
| Mihaiela Lupea, Anamaria Briciu, Istvan-Gergely Czibula, Gabriela Czibula – “SoftId: An autoencoder-based one-class classification model for software authorship identification“, 26th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2022), September 7-9, 2022, Procedia Computer Science 207, pp. 716-725 (B-ranked, indexed WoS) |
| Gabriela Czibula, Mihaiela Lupea, Anamaria Briciu – “Enhancing the performance of software authorship attribution using an ensemble of deep autoencoders“, Mathematics, Special Issue “Recent Advances in Artificial Intelligence and Machine Learning”, 2022, 10(15):2572 (A-ranked, indexed WoS, 2021 IF=2.592, Q1) |
| Gabriela Czibula, George Ciubotariu, Mariana Maier, Hannelore-Inge Lisei – “IntelliDaM: A machine learning based framework for enhancing the performance of decision-making processes“, A case study for educational data mining, IEEE Access, 2022, Volume 10, pp. 80651-80666 (B-ranked, indexed WoS, 2021 IF=3.476, Q2) |
2021
| Anamaria Briciu, Gabriela Czibula, Mihaiela Lupea – “AutoAt: A deep autoencoder-based classification model for supervised authorship attribution“, 25th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2021), September 8-10, 2021, Procedia Computer Science 192, pp. 397-406 (B-ranked, indexed WoS) |
| Vlad-Ioan Tomescu, Gabriela Czibula, Ștefan Nițică – “A study on using deep autoencoders for imbalanced binary classification“, 25th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2021), September 8-10, 2021, Procedia Computer Science 192, pp. 119-128 (B-ranked, indexed WoS) |
| George Ciubotariu, Vlad-Ioan Tomescu, Gabriela Czibula – “Enhancing the performance of image classification through features automatically learned from depth-maps“, 13th International Conference on Computer Vision Systems, September 22-24, 2021, LNCS 12899, pp. 68-81 (C-ranked) |
| Zsuzsanna Oneț-Marian, Gabriela Czibula, Mariana Maier – “Using self-organizing maps for comparing students’ academic performance in online and traditional learning environments“, Studies in Informatics and Control (SIC) journal, 30(4), 2021, pp. 17-28 (C-ranked, indexed WoS, IF 2020=1.649) |
| Maria-Mădălina Mircea, Rareș Boian and Gabriela Czibula – “A machine learning approach for data protection in virtual reality therapy applications“, 2021 IEEE 17th International Conference on Intelligent Computer Communication and Processing, 2021, pp. 367-374, DOI: 10.1109/ICCP53602.2021.9733574 (D-ranked, indexed WoS) |
| Diana-Lucia Miholca – “New Conceptual Cohesion Metrics: Assessment for Software Defect Prediction“, 23rd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2021, pp. 163-170, DOI: 10.1109/SYNASC54541.2021.00036 (D-ranked, indexed WoS) |
| Mariana-Ioana Maier, Gabriela Czibula, Zsuzsanna Oneț-Marian – “Towards Using Deep Autoencoders for Comparing Traditional and Synchronous Online Learning in Assessing Students’ Academic Performance“, Mathematics, Engineering Mathematics – special issue on Didactics and Technology in Mathematical Education, 2021, 9(22), 2870 (A-ranked, 2020 IF=2.258, Q1) |
Related Publications
These are precursor publications to the QuaDeeP project. They helped shape the project idea and serve to illustrate our research group’s previous efforts.
| Diana-Lucia Miholca, Gabriela Czibula, Vlad Tomescu – “COMET: A conceptual coupling based metrics suite for software defect prediction“, Procedia Computer Science 176, 2020, pp. 31-40. |
| Diana-Lucia Miholca, Zsuzsanna Oneț-Marian – “An analysis of aggregated coupling’s suitability for software defect prediction”, 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), 2020, pp. 141–148. |
| Mihai Teletin, Gabriela Czibula – “CVSimP: An approach for predicting proteins’ structural similarity using one-shot learning“, 2020 IEEE 14th International Symposium on Applied Computational Intelligence and Informatics (SACI), 2020, pp. 111-116. |
| Arthur-Jozsef Molnar, Simona Motogna – “Long-Term Evaluation of Technical Debt in Open-Source Software“, Proceedings of the 14th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM), 2020, pp. 1-9. |
| Arthur-Jozsef Molnar, Simona Motogna – “Longitudinal Evaluation of Open-source Software Maintainability“, Proceedings of the 15th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE 2020), 2020, pp. 120-131. |
| Istvan Gergely Czibula, Gabriela Czibula, Diana-Lucia Miholca, Zsuzsanna Onet-Marian. – “An aggregated coupling measure for the analysis of object-oriented software systems“, Journal of Systems and Software 148, 2019, pp. 1-20. |
| Diana-Lucia Miholca, Gabriela Czibula – “Software Defect Prediction Using a Hybrid Model Based on Semantic Features Learned from the Source Code“, International Conference on Knowledge Science, Engineering and Management, August 28-30, 2019, LNCS 11775, pp. 262–274. |
| Arthur-Jozsef Molnar, Alexandra Neamţu, Simona Motogna – “Longitudinal Evaluation of Software Quality Metrics in Open-Source Applications“, Proceedings of the 14th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE 2019), 2019, pp. 80–91. |
| Mihai Teletin, Gabriela Czibula, Carmina Codre – “AutoSimP: An approach for predicting proteins’ structural similarities using an ensemble of deep autoencoders“, International Conference on Knowledge Science, Engineering and Management, August 28-30, 2019, LNCS 11776, pp. 49–54. |
| Diana-Lucia Miholca, Gabriela Czibula, Istvan Gergely Czibula – “A novel approach for software defect prediction through hybridizing gradual relational association rules with artificial neural networks“, Information Sciences 441, 2018, pp. 152 – 170. |
| Istvan Gergely Czibula, Gabriela Czibula, Diana-Lucia Miholca – “Enhancing relational association rules with gradualness“, International Journal of Innovative Computing, Information and Control 13(1), 2017, pp. 289-305. |
| Gabriela Czibula, Zsuzsanna Marian, Istvan Gergely Czibula – “Detecting software design defects using relational association rule mining“, Knowledge and Information Systems 42(3), 2015, pp. 545–577. |
| Gabriela Czibula, Zsuzsanna Marian, Istvan Gergely Czibula – “Software defect prediction using relational association rule mining“, Information Sciences 264, 2014, pp. 260-278. |
| Gabriela Şerban, Alina Câmpan, Istvan Gergely Czibula – “A Programming Interface For Finding Relational Association Rules“, International Journal of Computers, Communications & Control 1(S), 2006, pp. 439-444. |