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Aly, M., Khomh, F., Guéhéneuc, Y.-G., Washizaki, H., & Yacout, S. (2019). Is fragmentation a threat to the success of the Internet of things? IEEE Internet of Things Journal, 6(1), 472-487. 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
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
Pan, W., Washizaki, H., Yoshioka, N., Fukazawa, Y., Khomh, F., & Guéhéneuc, Y.-G. (2023, December). A Machine Learning Based Approach to Detect Machine Learning Design Patterns [Paper]. 30th Asia-Pacific Software Engineering Conference (APSEC 2023), Seoul, Korea, Republic of Seoul. External link
Rahman, M. S., Khomh, F., Hamidi, A., Cheng, J., Antoniol, G., & Washizaki, H. (2023). Machine learning application development: practitioners insights. Software Quality Journal, 55 pages. External link
Tiwari, D., Washizaki, H., Fukazawa, Y., Fukuoka, T., Tamaki, J., Hosotani, N., Kohama, M., Guéhéneuc, Y.-G., & Khomh, F. (2020, May). Commit-defect and architectural metrics-based quality assessment of C language [Paper]. 15th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE 2020). External link
Wu, X., Li, H., Yoshioka, N., Washizaki, H., & Khomh, F. (2024, March). Refining GPT-3 Embeddings with a Siamese Structure for Technical Post Duplicate Detection [Paper]. 31st IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER 2024), Rovaniemi, Finland. External link
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. External link
Washizaki, H., Takeuchi, H., Khomh, F., Natori, N., Doi, T., & Okuda, S. (2020, September). Practitioners' insights on machine-learning software engineering design patterns: a preliminary study [Paper]. IEEE International Conference on Software Maintenance and Evolution (ICSME 2020). External link
Washizaki, H., Khomh, F., & Guéhéneuc, Y.-G. (2020, September). Software engineering patterns for machine learning applications (SEP4MLA) [Paper]. 9th Asian Conference on Pattern Languages of Program in 2020 (AsianPLoP 2020), Taipei, Taiwan (10 pages). Unavailable
Washizaki, H., Khomh, F., Guéhéneuc, Y.-G., Takeuchi, H., Okuda, S., Natori, N., & Shioura, N. (2020, October). Software Engineering Patterns for Machine Learning Applications (SEP4MLA) - Part 2 [Paper]. 27th Conference on Pattern Languages of Programs in 2020 (PLoP' 2020) (10 pages). Unavailable
Washizaki, H., Uchida, H., Khomh, F., & Guéhéneuc, Y.-G. (2019, December). Studying Software Engineering Patterns for Designing Machine Learning Systems [Paper]. 10th International Workshop on Empirical Software Engineering in Practice (IWESEP 2019), Tokyo, Japan. External link
Washizaki, H., Guéhéneuc, Y.-G., & Khomh, F. (2018). ProMeTA: A taxonomy for program metamodels in program reverse engineering. Empirical Software Engineering, 23(4), 2323-2358. Available
Washizaki, H., Guéhéneuc, Y.-G., & Khomh, F. (2016, October). A Taxonomy for Program Metamodels in Program Reverse Engineering [Paper]. IEEE International Conference on Software Maintenance and Evolution (ICSME 2016), Raleigh, NC. External link
