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Items where Author is "Nikanjam, Amin"

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Number of items: 34.

B

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

C

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

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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

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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., 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

M

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. Empirical Software Engineering, 28(3), 33 pages. 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

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

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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

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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

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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

S

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

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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

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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

List generated on: Sun Jul 13 07:22:32 2025 EDT