Exploring the use of automated API migrating techniques in practice: an experience report on Android. - Maxime Lamothe; Weiyi Shang (MSR, 2018)

A4: Automatically Assisting Android API Migrations Using Code Examples. - Maxime Lamothe; Weiyi Shang; Tse-Hsun Chen (CoRR, 2018)

Bridging the divide between API users and API developers by mining public code repositories. - Maxime Lamothe (ICSE (Companion Volume), 2020)

When APIs are intentionally bypassed: an exploratory study of API workarounds. - Maxime Lamothe; Weiyi Shang (ICSE, 2020)

Is Historical Data an Appropriate Benchmark for Reviewer Recommendation Systems? : A Case Study of the Gerrit Community. - Ian X. Gauthier; Maxime Lamothe; Gunter Mussbacher; Shane McIntosh (ASE, 2021)

Assessing the Exposure of Software Changes: The DiPiDi Approach. - Mehran Meidani; Maxime Lamothe; Shane McIntosh (CoRR, 2021)

Evaluating the impact of falsely detected performance bug-inducing changes in JIT models. - Sophia Quach; Maxime Lamothe; Bram Adams; Yasutaka Kamei; Weiyi Shang (Empir. Softw. Eng., 2021)

An empirical study on the use of SZZ for identifying inducing changes of non-functional bugs. - Sophia Quach; Maxime Lamothe; Yasutaka Kamei; Weiyi Shang (Empir. Softw. Eng., 2021)

Lessons from Eight Years of Operational Data from a Continuous Integration Service: An Exploratory Case Study of CircleCI. - Keheliya Gallaba; Maxime Lamothe; Shane McIntosh (ICSE, 2022)

How Does Code Reviewing Feedback Evolve?: A Longitudinal Study at Dell EMC. - Ruiyin Wen; Maxime Lamothe; Shane McIntosh (ICSE (SEIP), 2022)

Exploring the Notion of Risk in Code Reviewer Recommendation. - Farshad Kazemi; Maxime Lamothe; Shane McIntosh (ICSME, 2022)

A Systematic Review of API Evolution Literature. - Maxime Lamothe; Yann-Gaël Guéhéneuc; Weiyi Shang (ACM Comput. Surv., 2022)

Studying logging practice in test code. - Haonan Zhang; Yiming Tang 002; Maxime Lamothe; Heng Li 007; Weiyi Shang (Empir. Softw. Eng., 2022)

Assisting Example-Based API Misuse Detection via Complementary Artificial Examples. - Maxime Lamothe; Heng Li 007; Weiyi Shang (IEEE Trans. Software Eng., 2022)

A3: Assisting Android API Migrations Using Code Examples. - Maxime Lamothe; Weiyi Shang; Tse-Hsun Peter Chen (IEEE Trans. Software Eng., 2022)

Assessing the exposure of software changes. - Mehran Meidani; Maxime Lamothe; Shane McIntosh (Empir. Softw. Eng., 2023)

A Mutation-Guided Assessment of Acceleration Approaches for Continuous Integration: An Empirical Study of YourBase. - Zhili Zeng; Tao Xiao 001; Maxime Lamothe; Hideaki Hata; Shane McIntosh (MSR, 2024)

Re-Ranking Step by Step: Investigating Pre-Filtering for Re-Ranking with Large Language Models. - Baharan Nouriinanloo; Maxime Lamothe (CoRR, 2024)

Tracing Optimization for Performance Modeling and Regression Detection. - Kaveh Shahedi; Heng Li 007; Maxime Lamothe; Foutse Khomh (CoRR, 2024)

Mining Action Rules for Defect Reduction Planning. - Khouloud Oueslati; Gabriel Laberge; Maxime Lamothe; Foutse Khomh (Proc. ACM Softw. Eng., 2024)

How Trustworthy Is Your Continuous Integration (CI) Accelerator?: A Comparison of the Trustworthiness of CI Acceleration Products. - Zhili Zeng; Tao Xiao 001; Maxime Lamothe; Hideaki Hata; Shane McIntosh (IEEE Softw., 2024)

Communicating Study Design Trade-offs in Software Engineering. - Martin P. Robillard; Deeksha M. Arya; Neil A. Ernst; Jin L. C. Guo; Maxime Lamothe; Mathieu Nassif; Nicole Novielli; Alexander Serebrenik; Igor Steinmacher; Klaas-Jan Stol (ACM Trans. Softw. Eng. Methodol., 2024)

Characterizing the Prevalence, Distribution, and Duration of Stale Reviewer Recommendations. - Farshad Kazemi; Maxime Lamothe; Shane McIntosh (IEEE Trans. Software Eng., 2024)

What causes exceptions in machine learning applications? Mining machine learning-related stack traces on Stack Overflow. - Amin Ghadesi; Maxime Lamothe; Heng Li 007 (Empir. Softw. Eng., 2024)

InsightAI: Root Cause Analysis in Large Log Files with Private Data Using Large Language Model. - Maryam Ekhlasi; Anurag Prakash; Maxime Lamothe; Michel R. Dagenais (CAIN, 2025)

Automated, Unsupervised, and Auto-Parameterized Inference of Data Patterns and Anomaly Detection. - Qiaolin Qin; Heng Li 007; Ettore Merlo; Maxime Lamothe (ICSE, 2025)

JPerfEvo: A Tool for Tracking Method-Level Performance Changes in Java Projects. - Kaveh Shahedi; Maxime Lamothe; Foutse Khomh; Heng Li 007 (MSR, 2025)