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Abidi, M., Grichi, M., Khomh, F., & Guéhéneuc, Y.-G. (2025). Anti-patterns and Code Smells for Multi-language Systems. Dans Wallingford, E., Zdun, U., & Kohls, C. (édit.), Transactions on Pattern Languages of Programming V (p. 118-161). Lien externe
Aghili, R., Li, H., & Khomh, F. (juin 2025). Protecting Privacy in Software Logs: What Should Be Anonymized? [Communication écrite]. ACM International Conference on the Foundations of Software Engineering (FSE 2025), Trondheim, Norway. Publié dans Proceedings of the ACM on Software Engineering, 2(FSE). Lien externe
Aghili, R., Li, H., & Khomh, F. (2025). Protecting Privacy in Software Logs: What Should Be Anonymized? Proceedings of the ACM on software engineering., 2(FSE), 1317-1338. Lien externe
Ben Braiek, H., & Khomh, F. (2025). Machine learning robustness: a primer. Dans Lorenzi, M., & Zuluaga, M. A. (édit.), Trustworthy AI in Medical Imaging (p. 37-71). Lien externe
Brown, S., Khomh, F., Cavarroc-Weimer, M., Méndez, M. A., Martinu, L., & Sapieha, J.-E. (2025). Machine Learning Approach to the Assessment and Prediction of Solid Particle Erosion of Metals. Tribology International, 211, 110903 (13 pages). Lien externe
Da Silva, L. M. P., S. AMHI, J., & Khomh, F. (2025). LLMs and Stack Overflow Discussions: Reliability, Impact, and Challenges [Ensemble de données]. Lien externe
Da Silva, L. M. P., Samhi, J., & Khomh, F. (2025). LLMs and Stack Overflow discussions: Reliability, impact, and challenges. Journal of Systems and Software, 230, 112541 (21 pages). Lien externe
Degoot, A., Koné, I., Baichoo, S., Ngungu, M., Liku, N., Kumuthini, J., Nakatumba-Nabende, J., Khomh, F., & Bah, B. (2025). Health data issues in Africa: time for digitization, standardization and harmonization. Nature Communications, 16(1), 4 pages. Lien externe
Foalem, P. L., Da Silva, L. M. P., Khomh, F., Li, H., & Merlo, E. (2025). Logging requirement for continuous auditing of responsible machine learning-based applications. Empirical Software Engineering, 30(3), 97 (37 pages). Lien externe
Jamshidi, S., Amirnia, A., Nikanjam, A., Wazed Nafi, K., Khomh, F., & Keivanpour, S. (2025). Self-adaptive cyber defense for sustainable IoT: A DRL-based IDS optimizing security and energy efficiency. Journal of Network and Computer Applications, 104176. Lien externe
Jamshidi, S., Nikanjam, A., Wazed Nafi, K., & Khomh, F. (août 2025). Deep Reinforcement Learning-Based Intrusion Detection System: Defending Edge Gateways Against Mirai and Gafgyt [Communication écrite]. 12th International Conference on Future Internet of Things and Cloud (FiCloud 2025), Istanbul, Turkiye. Lien externe
Jamshidi, S., Nikanjam, A., Wazed Nafi, K., & Khomh, F. (juillet 2025). A Dynamic Security Pattern Selection Framework Using Deep Reinforcement Learning [Communication écrite]. International Conference on Software Services Engineering (SSE 2025), Helsinki, Finland. Lien externe
Jamshidi, S., Nikanjam, A., Wazed Nafi, K., & Khomh, F. (2025). Understanding the impact of IoT security patterns on CPU usage and energy consumption: a dynamic approach for selecting patterns with deep reinforcement learning. International Journal of Information Security, 24(2), 40 pages. Lien externe
Jamshidi, S., Nikanjam, A., Wazed Nafi, K., Khomh, F., & Rasta, R. (2025). Application of deep reinforcement learning for intrusion detection in Internet of Things: A systematic review. Internet of Things, 31, 101531 (29 pages). Lien externe
Jamshidi, S., Wazed Nafi, K., Nikanjam, A., & Khomh, F. (2025). Evaluating machine learning-driven intrusion detection systems in IoT: Performance and energy consumption. Computers & Industrial Engineering, 204, 111103 (17 pages). Lien externe
Kan, V., L. N. U., M., Berhe, S., Kari, C., Maynard, M., & Khomh, F. (2025). Automated UML visualization of software ecosystems: tracking versions, dependencies, and security updates. [Autre type de communication de conférence]. Procedia Computer Science, 257, 834-841. Présentée à 16th International Conference on Ambient Systems, Networks and Technologies Networks (ANT) / 8th International Conference on Emerging Data and Industry 4.0 (EDI40), Patras, Greece. Disponible
Liu, Y., Foundjem, A. T., Khomh, F., & Li, H. (2025). Adversarial attack classification and robustness testing for large language models for code. Empirical Software Engineering, 30(5). Lien externe
Majdinasab, V., Nikanjam, A., & Khomh, F. (2025). DeepCodeProbe: Evaluating Code Representation Quality in Models Trained on Code. Empirical Software Engineering, 30(6), 169 (53 pages). Lien externe
Majidi, F., Khomh, F., Li, H., & Nikanjam, A. (2025). An efficient model maintenance approach for MLOps. Empirical Software Engineering, 31(1), 48 pages. Lien externe
Manke, R., Wardat, M., Khomh, F., & Rajan, H. (avril 2025). Mock Deep Testing: Toward Separate Development of Data and Models for Deep Learning [Communication écrite]. 47th International Conference on Software Engineering (ICSE 2025), Ottawa, ON, Canada. Lien externe
Merzouk, M. A., Beurier, E., Yaich, R., Boulahia Cuppens, N., Cuppens, F., & Khomh, F. (juin 2025). Diffusion-Based Adversarial Purification for Intrusion Detection [Communication écrite]. 39th IFIP WG 11.3 Annual Conference on Data and Applications Security and Privacy (DBSec 2025), Gjøvik, Norway. Publié dans Lecture notes in computer science. Lien externe
Njoku, A. O., Li, H., & Khomh, F. (mai 2025). Kernel-Level Event-Based Performance Anomaly Detection in Software Systems under Varying Load Conditions [Communication écrite]. 16th International Conference on Performance Engineering (ICPE 2025), Toronto, ON, Canada. Lien externe
Nouwou Mindom, P. S., Da Silva, L. M. P., Nikanjam, A., & Khomh, F. (2025). Continuously Learning Bug Locations. ACM Transactions on Software Engineering and Methodology. Lien externe
Openja, M., Arcaini, P., Khomh, F., & Ishikawa, F. (2025). FairFLRep: Fairness aware fault localization and repair of Deep Neural Networks. ACM Transactions on Software Engineering and Methodology, 64 pages. Lien externe
Shah, M. B., Masudur Rahman, M., & Khomh, F. (2025). Towards understanding the impact of data bugs on deep learning models in software engineering. Empirical Software Engineering, 30(6), 168 (47 pages). Lien externe
Shahedi, K., Lamothe, M., Khomh, F., & Li, H. (avril 2025). JPerfEvo: A Tool for Tracking Method-Level Performance Changes in Java Projects [Communication écrite]. 22nd International Conference on Mining Software Repositories (MSR 2025), Ottawa, ON, Canada. Lien externe
Shahedi, K., Li, H., Lamothe, M., & Khomh, F. (2025). Tracing Optimization for Performance Modeling and Regression Detection. ACM Transactions on Software Engineering and Methodology. Lien externe
Tambon, F., Moradidakhel, A., Nikanjam, A., Khomh, F., Desmarais, M. C., & Antoniol, G. (2025). Bugs in large language models generated code: an empirical study. Empirical Software Engineering, 30(3), 48 pages. Lien externe
Tambon, F., Nikanjam, A., Zid, C., Khomh, F., & Antoniol, G. (2025). TaskEval: Assessing Difficulty of Code Generation Tasks for Large Language Models. ACM Transactions on Software Engineering and Methodology. Lien externe
Tantithamthavorn, C. K., Palomba, F., Khomh, F., & Chua, J. J. (2025). MLOps, LLMOps, FMOps, and beyond. IEEE Software, 42(1), 26-32. Lien externe
Veed, S. P., Daftary, S. M., Singh, B., Rudra, M., Berhe, S., Maynard, M., & Khomh, F. (février 2025). IoT Software Updates: User Perspectives in the Context of NIST IR 8259A [Communication écrite]. Workshop on Security and Privacy in Standardized IoT (SDIoTSec 2025), San Diego, CA, USA (5 pages). Lien externe
Verdet, A., Hamdaqa, M., Da Silva, L. M. P., & Khomh, F. (2025). Assessing the adoption of security policies by developers in terraform across different cloud providers. Empirical Software Engineering, 30(3). Disponible
Verdet, A., Hamdaqa, M., Da Silva, L. M. P., & Khomh, F. (2025). Erratum: Assessing the adoption of security policies by developers in terraform across different cloud providers. Empirical Software Engineering, 30(6), 74 (1 page). Lien externe
Wu, X., Li, H., & Khomh, F. (2025). What information contributes to log-based anomaly detection? Insights from a configurable transformer-based approach. Automated Software Engineering, 32(2), 29 pages. Lien externe
