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Maxime LamotheDepartment of Computer Engineering and Software Engineering
Profile in the Directory of Experts of Polytechnique Montréal
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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). External link
Ghadesi, A., Li, H., & Lamothe, M. (2023). What Causes Exceptions in Machine Learning Applications? Mining Machine Learning-Related Stack Traces on Stack Overflow [Dataset]. External link
Gallaba, K., Lamothe, M., & McIntosh, S. (2022, May). Lessons from eight years of operational data from a continuous integration service: An exploratory case study of CircleCI [Paper]. IEEE/ACM 44th International Conference on Software Engineering (ICSE 2022), Pittsburgh, PA, USA. External link
Gauthier, I. X., Lamothe, M., Mussbacher, G., & McIntosh, S. (2021, November). Is Historical Data an Appropriate Benchmark for Reviewer Recommendation Systems? : AA Case Study of the Gerrit Community [Paper]. 36th IEEE/ACM International Conference on Automated Software Engineering (ASE 2021), Melbourne, Australia. External link
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). External link
Kazemi, F., Lamothe, M., & McIntosh, S. (2024). Replication Package and Online Appendix for "Characterizing the impact, distribution, and duration of stale reviewer recommendations" [Dataset]. External link
Kazemi, F., Lamothe, M., & McIntosh, S. (2022). Dataset of the study "Exploring the Notion of Risk in Reviewer Recommendation" [Dataset]. External link
Kazemi, F., Lamothe, M., & McIntosh, S. (2022, October). Exploring the Notion of Risk in Code Reviewer Recommendation [Paper]. IEEE International Conference on Software Maintenance and Evolution (ICSME 2022), Limassol, Cyprus. External link
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. External link
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. External link
Lamothe, M., Gueheneuc, Y. G., & Shang, W. (2021). A Systematic Review of API Evolution Literature. ACM Computing Surveys, 54(8), 1-36. External link
Lamothe, M. (2020, June). Bridging the divide between API users and API developers by mining public code repositories [Paper]. 42nd ACM/IEEE International Conference on Software Engineering, Seoul, South Korea. External link
Lamothe, M., & Shang, W. (2020, June). When APIs are intentionally bypassed [Paper]. 42nd ACM/IEEE International Conference on Software Engineering, Seoul, South Korea. External link
Lamothe, M., & Shang, W. (2018, May). Exploring the use of automated API migrating techniques in practice [Paper]. 15th International Conference on Mining Software Repositories, Gothenburg, Sweden. External link
Meidani, M., Lamothe, M., & McIntosh, S. (2023). Assessing the exposure of software changes: The DiPiDi approach. Empirical Software Engineering, 28(2), 36 pages. External link
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. Available
Qin, Q., Li, H., Merlo, E., & Lamothe, M. (2025). Automated, Unsupervised, and Auto-parameterized Inference of Data Patterns and Anomaly Detection [Dataset]. External link
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). External link
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). External link
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). External link
Wen, R., Lamothe, M., & McIntosh, S. (2022, May). How does code reviewing feedback evolve?: A longitudinal study at Dell EMC [Paper]. IEEE/ACM 44th International Conference on Software Engineering: Software Engineering in Practice (ICSE 2022), Pittsburgh, PA, USA. External link
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). External link
Zeng, Z., Xiao, T., Lamothe, M., Hata, H., & McIntosh, S. (2024, April). A Mutation-Guided Assessment of Acceleration Approaches for Continuous Integration: An Empirical Study of YourBase [Paper]. 2024 IEEE/ACM 21st International Conference on Mining Software Repositories (MSR 2024), Lisbon, Portugal. External link
Zeng, Z., Xiao, T., Lamothe, M., Hata, H., & McIntosh, S. (2023). Online appendix. Zenodo (CERN European Organization for Nuclear Research). External link
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). External link
