Documents dont l'auteur est "Lamothe, Maxime"

Maxime LamotheDépartement de génie informatique et génie logicielVoir sa fiche dans le Répertoire des Expertises de Polytechnique Montréal

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Oueslati, K., Laberge, G., Lamothe, M., & Khomh, F. (2024). Mining Action Rules for Defect Reduction Planning. Proceedings of the ACM on Software Engineering, 1(FSE), 2309-2331. Lien externe

Kazemi, F., Lamothe, M., & McIntosh, S. (2024). Characterizing the Prevalence Distribution and Duration of Stale Reviewer Recommendations. IEEE Transactions on Software Engineering, 3422369 (14 pages). Lien externe

Robillard, M. P., Arya, D. M., Ernst, N. A., Guo, J. L. C., Lamothe, M., Nassif, M., Novielli, N., Serebrenik, A., Steinmacher, I., & Stol, K.-J. (2024). Communicating Study Design Trade-offs in Software Engineering. ACM Transactions on Software Engineering and Methodology, 33(5), 112 (10 pages). Lien externe

Zeng, Z., Xiao, T., Lamothe, M., Hata, H., & McIntosh, S. (2024). How Trustworthy is Your CI Accelerator? A Comparison of the Trustworthiness of CI Acceleration Products. IEEE Software, 3395616 (6 pages). Lien externe

Zeng, Z., Xiao, T., Lamothe, M., Hata, H., & McIntosh, S. (avril 2024). A Mutation-Guided Assessment of Acceleration Approaches for Continuous Integration: An Empirical Study of YourBase [Communication écrite]. 2024 IEEE/ACM 21st International Conference on Mining Software Repositories (MSR 2024), Lisbon, Portugal. Lien externe

Kazemi, F., Lamothe, M., & McIntosh, S. (2024). Replication Package and Online Appendix for "Characterizing the impact, distribution, and duration of stale reviewer recommendations" [Ensemble de données]. Lien externe

Ghadesi, A., Lamothe, M., & Li, H. (2024). What causes exceptions in machine learning applications? Mining machine learning-related stack traces on Stack Overflow. Empirical Software Engineering, 29, 107 (37 pages). Lien externe

Ghadesi, A., Li, H., & Lamothe, M. (2023). What Causes Exceptions in Machine Learning Applications? Mining Machine Learning-Related Stack Traces on Stack Overflow [Ensemble de données]. Lien externe

Zeng, Z., Xiao, T., Lamothe, M., Hata, H., & McIntosh, S. (2023). Online appendix. Zenodo (CERN European Organization for Nuclear Research). Lien externe

Meidani, M., Lamothe, M., & McIntosh, S. (2023). Assessing the exposure of software changes: The DiPiDi approach. Empirical Software Engineering, 28(2), 36 pages. Lien externe

Wen, R., Lamothe, M., & McIntosh, S. (mai 2022). How Does Code Reviewing Feedback Evolve?: A Longitudinal Study at Dell EMC [Communication écrite]. IEEE/ACM 44th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP 2022), Pittsburgh, PA, USA. Lien externe

Lamothe, M., Shang, W., & Chen, T.-H. P. (2022). A3: Assisting Android API Migrations Using Code Examples. IEEE Transactions on Software Engineering, 48(2), 417-431. Lien externe

Lamothe, M., Li, H., & Shang, W. (2022). Assisting Example-based API Misuse Detection via Complementary Artificial Examples. IEEE Transactions on Software Engineering, 48(9), 3410-3422. Lien externe

Kazemi, F., Lamothe, M., & McIntosh, S. (2022). Dataset of the study "Exploring the Notion of Risk in Reviewer Recommendation" [Ensemble de données]. Lien externe

Kazemi, F., Lamothe, M., & McIntosh, S. (octobre 2022). Exploring the Notion of Risk in Code Reviewer Recommendation [Communication écrite]. IEEE International Conference on Software Maintenance and Evolution (ICSME 2022), Limassol, Cyprus. Lien externe

Wen, R., Lamothe, M., & McIntosh, S. (mai 2022). How does code reviewing feedback evolve?: A longitudinal study at Dell EMC [Communication écrite]. IEEE/ACM 44th International Conference on Software Engineering: Software Engineering in Practice (ICSE 2022), Pittsburgh, PA, USA. Lien externe

Gallaba, K., Lamothe, M., & McIntosh, S. (mai 2022). Lessons from eight years of operational data from a continuous integration service: An exploratory case study of CircleCI [Communication écrite]. IEEE/ACM 44th International Conference on Software Engineering (ICSE 2022), Pittsburgh, PA, USA. Lien externe

Zhang, H., Tang, Y., Lamothe, M., Li, H., & Shang, W. (2022). Studying logging practice in test code. Empirical Software Engineering, 27(4), 83 (45 pages). Lien externe

Quach, S., Lamothe, M., Kamei, Y., & Shang, W. (2021). An empirical study on the use of SZZ for identifying inducing changes of non-functional bugs. Empirical Software Engineering, 26(4). Lien externe

Quach, S., Lamothe, M., Adams, B., Kamei, Y., & Shang, W. (2021). Evaluating the impact of falsely detected performance bug-inducing changes in JIT models. Empirical Software Engineering, 26(5). Lien externe

Gauthier, I. X., Lamothe, M., Mussbacher, G., & McIntosh, S. (novembre 2021). Is Historical Data an Appropriate Benchmark for Reviewer Recommendation Systems? : AA Case Study of the Gerrit Community [Communication écrite]. 36th IEEE/ACM International Conference on Automated Software Engineering (ASE 2021), Melbourne, Australia. Lien externe

Lamothe, M., Gueheneuc, Y. G., & Shang, W. (2021). A Systematic Review of API Evolution Literature. ACM Computing Surveys, 54(8), 1-36. Lien externe

Lamothe, M. (juin 2020). Bridging the divide between API users and API developers by mining public code repositories [Communication écrite]. 42nd ACM/IEEE International Conference on Software Engineering, Seoul, South Korea. Lien externe

Lamothe, M., & Shang, W. (juin 2020). When APIs are intentionally bypassed [Communication écrite]. 42nd ACM/IEEE International Conference on Software Engineering, Seoul, South Korea. Lien externe

Lamothe, M., & Shang, W. (mai 2018). Exploring the use of automated API migrating techniques in practice [Communication écrite]. 15th International Conference on Mining Software Repositories, Gothenburg, Sweden. Lien externe