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Humeniuk, D., Khomh, F., & Antoniol, G. (2024). Reinforcement Learning Informed Evolutionary Search for Autonomous Systems Testing. ACM Transactions on Software Engineering and Methodology. Lien externe
Muse, B. A., Nafi, K. W., Khomh, F., & Antoniol, G. (2024). Data-access performance anti-patterns in data-intensive systems. Empirical Software Engineering, 29, 144 (35 pages). Lien externe
Orlando, L., Maiello, E., Orditura, M., Diana, A., Antoniol, G., Morritti, M. G., Aieta, M., Ciccarese, M., Pisconti, S., Bordonaro, R., Russo, A., Febbraro, A., Schiavone, P., Quaranta, A., Caliolo, C., Loparco, D., Cinefra, M., Colucci, G., & Cinieri, S. (2024). Phase II randomized trial comparing metronomic anthracycline-containing chemotherapy versus standard schedule in untreated HER2 negative advanced breast cancer: activity and quality of life results of the GOIM 21003 trial. The Breast, 75, 103725 (7 pages). Disponible
Tambon, F., Khomh, F., & Antoniol, G. (2024). GIST : Generated Inputs Sets Transferability in Deep Learning. ACM Transactions on Software Engineering and Methodology. Lien externe
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). Lien externe
Zid, C., Belias, F., Di Penta, M., Khomh, F., & Antoniol, G. (mars 2024). List Comprehension Versus for Loops Performance in Real Python Projects: Should we Care? [Communication écrite]. 31st IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER 2024), Rovaniemi, Finland. Lien externe
Zid, C., Zampetti, F., Antoniol, G., & Di Penta, M. (avril 2024). A study on the pythonic functional constructs' understandability [Présentation]. Dans 2024 IEEE/ACM 46th International Conference on Software Engineering (ICSE 2024), Lisbon, Portugal (13 pages). Lien externe
Zid, C., Zampetti, F., Antoniol, G., & Di Penta, M. (2024). "Replication package for the paper: ""A Study on the Pythonic Functional Constructs' Understandability""" [Ensemble de données]. Lien externe
Zid, C., Zampetti, F., Antoniol, G., & Di Penta, M. (2024). Replication package for the paper: "A Study on the Pythonic Functional Constructs' Understandability" [Ensemble de données]. Lien externe
