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Abidi, M., Rahman, M. S., Openja, M., & Khomh, F. (2022). Multi-language design smells: a backstage perspective. Empirical Software Engineering, 27(5), 52 pages. Lien externe
Ben Braiek, H., Reid, T., & Khomh, F. (2022). Physics-guided adversarial machine learning for aircraft systems simulation. IEEE Transactions on Reliability, 72(3), 1161-1175. Lien externe
Ben Braiek, H., Tfaily, A., Khomh, F., Reid, T., & Guida, C. (octobre 2022). SmOOD: Smoothness-based Out-of-Distribution Detection Approach for Surrogate Neural Networks in Aircraft Design [Communication écrite]. 37th IEEE/ACM International Conference on Automated Software Engineering (ASE 2022), Rochester, MI, USA (13 pages). Lien externe
Humeniuk, D., Antoniol, G., & Khomh, F. (mai 2022). ***AmbieGen tool at the SBST 2022 Tool Competition [Communication écrite]. 15th Search-Based Software Testing Workshop (SBST 2022). Lien externe
Humeniuk, D., Khomh, F., & Antoniol, G. (2022). A search-based framework for automatic generation of testing environments for cyber-physical systems. Information and Software Technology, 149, 106936 (17 pages). Lien externe
Ikama, A., Du, V., Belias, P., Muse, B. A., Khomh, F., & Hamdaqa, M. (octobre 2022). Revisiting the Impact of Anti-patterns on Fault-Proneness: A Differentiated Replication [Communication écrite]. 22nd IEEE International Working Conference on Source Code Analysis and Manipulation (SCAM 2022), Limassol, Cyprus. Lien externe
Jebnoun, H., Rahman, M. S., Khomh, F., & Muse, B. A. (2022). Clones in deep learning code: what, where, and why? Empirical Software Engineering, 27(4). Lien externe
Khomh, F. (novembre 2022). Data quality and model under-specification issues (keynote) [Communication écrite]. 2nd International Workshop on Software Engineering and AI for Data Quality in Cyber-Physical Systems/Internet of Things (SEA4DQ 2022), Singapore (2 pages). Lien externe
Majidi, F., Openja, M., Khomh, F., & Li, H. (octobre 2022). An Empirical Study on the Usage of Automated Machine Learning Tools [Communication écrite]. IEEE International Conference on Software Maintenance and Evolution (ICSME 2022), Limassol, Cyprus. Lien externe
Marhaba, M., Merlo, E., Khomh, F., & Antoniol, G. (mai 2022). Identification of out-of-distribution cases of CNN using class-based surprise adequacy [Communication écrite]. IEEE/ACM 1st International Conference on AI Engineering - Software Engineering for AI (CAIN 2022), Pittsburgh, PA, USA. Lien externe
Muse, B. A., Khomh, F., & Antoniol, G. (mars 2022). Do developers refactor data access code? An empirical study [Communication écrite]. IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER 2022), Honolulu, HI, USA. Lien externe
Muse, B. A., Nagy, C., Cleve, A., Khomh, F., & Antoniol, G. (2022). FIXME: synchronize with database! An empirical study of data access self-admitted technical debt. Empirical Software Engineering, 27(6), 42 pages. Lien externe
Nikanjam, A., Ben Braiek, H., Morovati, M. M., & Khomh, F. (2022). Automatic Fault Detection for Deep Learning Programs Using Graph Transformations. ACM Transactions on Software Engineering and Methodology, 31(1), 1-27. Lien externe
Nikanjam, A., Morovati, M. M., Khomh, F., & Ben Braiek, H. (2022). Faults in deep reinforcement learning programs: a taxonomy and a detection approach. Automated Software Engineering, 29(1), 8 (32 pages). Lien externe
Openja, M., Majidi, F., Khomh, F., Chembakottu, B., & Li, H. (juin 2022). Studying the Practices of Deploying Machine Learning Projects on Docker [Communication écrite]. 26th ACM International Conference on Evaluation and Assessment in Software Engineering (EASE 2022), Gothenburg, Sweden. Lien externe
Openja, M., Morovati, M. M., An, L., Khomh, F., & Abidi, M. (2022). Technical debts and faults in open-source quantum software systems: An empirical study. Journal of Systems and Software, 193, 28 pages. Lien externe
Openja, M., Nikanjam, A., Yahmed, A. H., Khomh, F., & Jiang, Z. M. J. (octobre 2022). An Empirical Study of Challenges in Converting Deep Learning Models [Communication écrite]. 39th IEEE International Conference on Software Maintenance and Evolution (ICSME 2022), Limassol, Cyprus. Lien externe
Rahman, M. M., Khomh, F., & Castelluccio, M. (2022). Works for me! Cannot reproduce: A large scale empirical study of non-reproducible bugs. Empirical Software Engineering, 27(5), 111 (45 pages). Lien externe
Rahman, M. S., Khomh, F., Rivera, E., Guéhéneuc, Y.-G., & Lehnert, B. (mai 2022). Challenges in machine learning application development : an industrial experience report [Communication écrite]. IEEE/ACM 1st International Workshop on Software Engineering for Responsible Artificial Intelligence (SE4RAI 2022), Pittsburgh, PA, USA. Lien externe
Ramkisoen, P. K., Businge, J., Van Bladel, B., Decan, A., Demeyer, S., De Roover, C., & Khomh, F. (novembre 2022). PaReco: patched clones and missed patches among the divergent variants of a software family [Communication écrite]. 30th ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2022), Singapore, Singapore. Lien externe
Roy, S., Laberge, G., Roy, B., Khomh, F., Nikanjam, A., & Mondal, S. (octobre 2022). Why Don't XAI Techniques Agree? Characterizing the Disagreements Between Post-hoc Explanations of Defect Predictions [Communication écrite]. IEEE International Conference on Software Maintenance and Evolution (ICSME 2022), Limassol, Cyprus. Lien externe
Tambon, F., Laberge, G., An, L., Nikanjam, A., Mindom, P. S. N., Pequignot, Y., Khomh, F., Antoniol, G., Merlo, E., & Laviolette, F. (2022). How to certify machine learning based safety-critical systems? A systematic literature review. Automated Software Engineering, 29(2). Lien externe
Tidjon, L. N., & Khomh, F. (2022). The different faces of AI ethics acrosse the world : a principle-to-pratice gap analysis. IEEE Transactions on Artificial Intelligence, 4(4), 820-839. Lien externe
Uddin, G., Guéhénuc, Y.-G., Khomh, F., & Roy, C. K. (2022). An Empirical Study of the Effectiveness of an Ensemble of Stand-alone Sentiment Detection Tools for Software Engineering Datasets. ACM Transactions on Software Engineering and Methodology, 31(3), 1-38. Lien externe
Washizaki, H., Khomh, F., Gueheneuc, Y. G., Takeuchi, H., Natori, N., Doi, T., & Okuda, S. (2022). Software-Engineering Design Patterns for Machine Learning Applications. Computer, 55(3), 30-39. Lien externe
Yahmed, A. H., Ben Braiek, H., Khomh, F., Bouzidi, S., & Zaatour, R. (2022). DiverGet: a Search-Based Software Testing approach for Deep Neural Network Quantization assessment. Empirical Software Engineering, 27(7), 193 (32 pages). Lien externe
