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Abbassi, A. A., Da Silva, L. M. P., Nikanjam, A., & Khomh, F. (2025, September). A Taxonomy of Inefficiencies in LLM-Generated Python Code [Paper]. International Conference on Software Maintenance and Evolution (ICSME 2025), Auckland, New Zealand. External link
Bouchoucha, R., Haj Yahmed, A., Patil, D., Rajendran, J., Nikanjam, A., Anbil Parthipan, S. C., & Khomh, F. (2024, October). Toward Debugging Deep Reinforcement Learning Programs with RLExplorer [Paper]. IEEE International Conference on Software Maintenance and Evolution (ICSME 2024), Flagstaff, AZ, USA. External link
Chitsazian, Z., Sedighian Kashi, S., & Nikanjam, A. (2026). Detecting concept drift in just-in-time software defect prediction using model interpretation. International Journal of Systems Assurance Engineering and Management, 22 pages. External link
Côté, P.-O., Nikanjam, A., Ahmed, N., Humeniuk, D., & Khomh, F. (2024). Data cleaning and machine learning: a systematic literature review. Automated Software Engineering, 31(2), 54 (75 pages). External link
Côté, P.-O., Nikanjam, A., Bouchoucha, R., Basta, I., Abidi, M., & Khomh, F. (2024). Quality issues in machine learning software systems. Empirical Software Engineering, 29(6), 149 (47 pages). External link
Islam, M. R., Roy, B., Hassan, M., & Nikanjam, A. (2024, November). Just-in-Time and Real-Time Bug-Inducing Commit Prediction Using a Federated Learning Approach [Paper]. 34th International Conference on Collaborative Advances in Software and COmputiNg (CASCON 2024), Toronto, ON, Canada. External link
Jamshidi, S., Abdul Wahab, O., Herrero, R., Khomh, F., Bellaïche, M., Keivanpour, S., Shahabi, N., Nikanjam, A., & Wazed Nafi, K. (2026). Think Fast: Real-Time IoT Intrusion Reasoning Using IDS and LLMs at the Edge Gateway. IEEE Internet of Things Journal, 30 pages. External link
Jamshidi, S., Shahabi, N., Nikanjam, A., Wazed Nafi, K., Khomh, F., & Fung, C. (2025). The role of Large Language Models in IoT security: A systematic review of advances, challenges, and opportunities [Discussion or Letter]. Internet of Things, 34, 101735 (31 pages). External link
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). External link
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. External link
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. External link
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). External link
Jamshidi, S., Nikanjam, A., Wazed Nafi, K., & Khomh, F. (2025, August). Deep Reinforcement Learning-Based Intrusion Detection System: Defending Edge Gateways Against Mirai and Gafgyt [Paper]. 12th International Conference on Future Internet of Things and Cloud (FiCloud 2025), Istanbul, Turkiye. External link
Jamshidi, S., Nikanjam, A., Wazed Nafi, K., & Khomh, F. (2025, July). A Dynamic Security Pattern Selection Framework Using Deep Reinforcement Learning [Paper]. International Conference on Software Services Engineering (SSE 2025), Helsinki, Finland. External link
Jamshidi, S., Amirnia, A., Nikanjam, A., & Khomh, F. (2024, April). Enhancing Security and Energy Efficiency of Cyber-Physical Systems using Deep Reinforcement Learning [Paper]. 15th International Conference on Ambient Systems, Networks and Technologies Networks (ANT 2024) / The 7th International Conference on Emerging Data and Industry 4.0 (EDI40 2024), Hasselt, Belgium. Published in Procedia Computer Science, 238. External link
Jamshidi, S., Nikanjam, A., Hamdaqa, M. A., & Khomh, F. (2023). Attack Detection by Using Deep Learning for Cyber-Physical System. In Artificial Intelligence for Cyber-Physical Systems Hardening (Vol. 2, pp. 155-179). External link
Majidi, F., Khomh, F., Li, H., & Nikanjam, A. (2025). An efficient model maintenance approach for MLOps. Empirical Software Engineering, 31(1), 48 pages. External link
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). External link
Majdinasab, V., Nikanjam, A., & Khomh, F. (2024). Trained Without My Consent: Detecting Code Inclusion In Language Models Trained on Code. ACM Transactions on Software Engineering and Methodology. External link
Morovati, M. M., Nikanjam, A., Tambon, F., Khomh, F., & Jiang, Z. M. (2024). Bug characterization in machine learning-based systems. Empirical Software Engineering, 29(1), 14 (29 pages). External link
Morovati, M. M., Tambon, F., Taraghi, M., Nikanjam, A., & Khomh, F. (2024). Common challenges of deep reinforcement learning applications development: an empirical study. Empirical Software Engineering, 29, 95 (33 pages). External link
Moradidakhel, A., Nikanjam, A., Majdinasab, V., Khomh, F., & Desmarais, M. C. (2024). Effective test generation using pre-trained Large Language Models and mutation testing. Information and Software Technology, 171, 107468 (17 pages). External link
Moradidakhel, A., Nikanjam, A., Khomh, F., Desmarais, M. C., & Washizaki, H. (2024). Generative AI for Software Development: A Family of Studies on Code Generation. In Generative AI for Effective Software Development (pp. 151-172). External link
Mindom, P. S. N., Nikanjam, A., & Khomh, F. (2024). Harnessing pre-trained generalist agents for software engineering tasks. Empirical Software Engineering, 30(1). External link
Moradidakhel, A., Nikanjam, A., Khomh, F., Desmarais, M. C., & Washizaki, H. (2024). An Overview on Large Language Models. In Generative AI for Effective Software Development (pp. 3-21). External link
Morovati, M. M., Nikanjam, A., Khomh, F., & Jiang, Z. M. (2023). Bugs in machine learning-based systems: a faultload benchmark [Dataset]. External link
Morovati, M. M., Nikanjam, A., Khomh, F., & Jiang, Z. M. (2023). Bugs in machine learning-based systems: a faultload benchmark. Empirical Software Engineering, 28, 62 (33 pages). External link
Moradidakhel, A., Majdinasab, V., Nikanjam, A., Khomh, F., Desmarais, M. C., & Jiang, Z. M. (2023). GitHub Copilot AI pair programmer: Asset or Liability? Journal of Systems and Software, 203, 111734 (23 pages). External link
Mahdavimoghadam, M., Nikanjam, A., & Abdoos, M. (2022). Improved reinforcement learning in cooperative multi-agent environments using knowledge transfer. Journal of Supercomputing, 78(8), 10455-10479. External link
Mindom, P. S. N., Nikanjam, A., Khomh, F., & Mullins, J. (2021, December). On Assessing The Safety of Reinforcement Learning algorithms Using Formal Methods [Paper]. 21st International Conference on Software Quality, Reliability and Security (QRS 2021), Hainan, China. External link
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. External link
Nouwou Mindom, P. S., Nikanjam, A., & Khomh, F. (2023). A comparison of reinforcement learning frameworks for software testing tasks. Empirical Software Engineering, 28(5), 111 (76 pages). External link
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. External link
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). External link
Nikanjam, A., & Khomh, F. (2021, September). Design Smells in Deep Learning Programs: An Empirical Study [Paper]. IEEE International Conference on Software Maintenance and Evolution (ICSME 2021), Luxembourg, Netherlands. External link
Openja, M., Nikanjam, A., Yahmed, A. H., Khomh, F., & Jiang, Z. M. J. (2022, October). An Empirical Study of Challenges in Converting Deep Learning Models [Paper]. 39th IEEE International Conference on Software Maintenance and Evolution (ICSME 2022), Limassol, Cyprus. External link
Roy, S., Laberge, G., Roy, B., Khomh, F., Nikanjam, A., & Mondal, S. (2022, October). Why Don't XAI Techniques Agree? Characterizing the Disagreements Between Post-hoc Explanations of Defect Predictions [Paper]. IEEE International Conference on Software Maintenance and Evolution (ICSME 2022), Limassol, Cyprus. External link
Rivera-Landos, E., Khomh, F., & Nikanjam, A. (2021, December). The Challenge of Reproducible ML: An Empirical Study on The Impact of Bugs [Paper]. 21st International Conference on Software Quality, Reliability and Security (QRS 2021), Hainan, China. External link
Shajoonnezhad, N., & Nikanjam, A. (2022). A stochastic variance-reduced coordinate descent algorithm for learning sparse Bayesian network from discrete high-dimensional data. International Journal of Machine Learning and Cybernetics, 14(3), 947-958. External link
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. External link
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. External link
Tambon, F., Nikanjam, A., An, L., Khomh, F., & Antoniol, G. (2024). Silent bugs in deep learning frameworks: an empirical study of Keras and TensorFlow. Empirical Software Engineering, 29(1), 10 (34 pages). External link
Tambon, F., Majfinasab, V., Nikanjam, A., Khomh, F., & Antoniol, G. (2023, April). Mutation testing of deep reinforcement learning based on real faults [Paper]. 16th IEEE Conference on Software Testing, Verification and Validation (ICST 2023), Dublin, Ireland. External link
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). External link
Yahmed, A. H., Allah Abbassi, A., Nikanjam, A., Li, H., & Khomh, F. (2023, October). Deploying deep reinforcement learning systems: a taxonomy of challenges [Paper]. IEEE International Conference on Software Maintenance and Evolution (ICSME 2023), Bogota, Colombia. External link
