This program is tentative and subject to change.
Mon 13 AprDisplayed time zone: Brasilia, Distrito Federal, Brazil change
09:00 - 10:30 | |||
09:00 40mDay opening | Opening Session MSR Program | ||
09:40 50mKeynote | The Role of an Empirical Software Engineering Researcher in the Age of Generative AI Keynotes Emerson Murphy-Hill Microsoft | ||
11:00 - 12:30 | |||
11:00 10mResearch paper | Where Do Smart Contract Security Analyzers Fall Short? Technical Papers DOI Pre-print | ||
11:10 10mTalk | An Empirical Study of Vulnerabilities in Python Packages and Their Detection Technical Papers Haowei Quan Monash University, Junjie Wang Tianjin University, Xinzhe Li College of Intelligence and Computing, Tianjin University, Terry Yue Zhuo Monash University and CSIRO's Data61, Xiao Chen University of Newcastle, Xiaoning Du Monash University | ||
11:20 10mTalk | Does Programming Language Matter? An Empirical Study of Fuzzing Bug Detection Technical Papers Tatsuya Shirai Nara Institute of Science and Technology, Olivier Nourry The University of Osaka, Yutaro Kashiwa Nara Institute of Science and Technology, Kenji Fujiwara Nara Women’s University, Hajimu Iida Nara Institute of Science and Technology | ||
11:30 10mTalk | An Empirical Study on Line-Level Software Defect Prediction Technical Papers Enci Zhang Beijing Jiaotong University, Yutong Jiang Beijing Jiaotong University, Tianmeng Zhang Beijing Jiaotong University, Haonan Tong Beijing Jiaotong University | ||
11:40 10mTalk | Characterizing and Modeling the GitHub Security Advisories Review Pipeline Technical Papers Claudio Segal UFF, Paulo Segal UFF, Carlos Eduardo de Schuller Banjar UFRJ, Felipe Paixão Federal University of Bahia (UFBA), Hudson Silva Borges UFMS, Paulo Silveira Neto Federal University Rural of Pernambuco, Eduardo Santana de Almeida Federal University of Bahia, Joanna C. S. Santos University of Notre Dame, Anton Kocheturov Siemens Technology, Gaurav Kumar Srivastava Siemens, Daniel Sadoc Menasche UFRJ, Brazil Pre-print | ||
11:50 10mTalk | Linux Kernel Recency Matters, CVE Severity Doesn’t, and History Fades Technical Papers Piotr Przymus Nicolaus Copernicus University in Toruń, Poland, Witold Weiner Nicolaus Copernicus University in Toruń and Adtran Networks Sp. z o.o, Krzysztof Rykaczewski Nicolaus Copernicus University in Toruń, Poland, Gunnar Kudrjavets Amazon Web Services, USA Pre-print | ||
12:00 10mTalk | Beyond Single Code Changes: An Empirical Study of Topic-Based Code Review Practices in Gerrit for OpenStack Technical Papers Moataz Chouchen Concordia University, Mahi Begoug ETS Montreal, Ali Ouni Ecole de Technologie Superieure (ETS) | ||
12:10 10mTalk | LogSieve: Task-Aware CI Log Reduction for Sustainable LLM-Based Analysis Technical Papers Marcus Barnes University of Toronto, Taher A. Ghaleb Trent University, Safwat Hassan University of Toronto Pre-print | ||
12:20 5mTalk | Finding Important Stack Frames in Large Systems Industry Track Aleksandr Khvorov JetBrains; Constructor University Bremen, Yaroslav Golubev JetBrains Research, Denis Sushentsev JetBrains | ||
12:25 5mTalk | Stop Comparing Apples and Oranges: Matching for Better Results in Mining Software Repositories Studies Technical Papers Sabato Nocera University of Salerno, Nyyti Saarimäki University of Luxembourg, Valentina Lenarduzzi University of Southern Denmark, Davide Taibi University of Southern Denmark and University of Oulu, Sira Vegas Universidad Politecnica de Madrid | ||
14:00 - 15:30 | |||
14:00 90mPoster | Session 2 - Posters MSR Program | ||
14:00 90mTalk | When AI Agents Touch CI/CD Configurations: Frequency and Success Mining Challenge Taher A. Ghaleb Trent University Pre-print | ||
14:00 90mTalk | Fingerprinting AI Coding Agents on GitHub Mining Challenge Taher A. Ghaleb Trent University Pre-print | ||
14:00 90mTalk | When AI Code Doesn’t Stick: An Empirical Study on Reverted Changes Introduced by AI Coding Agents Mining Challenge Issam Oukay Department of Software and IT Engineering, ETS Montreal, University of Quebec, Montreal, Canada, Mahi Begoug ETS Montreal, Moataz Chouchen Concordia University, Ali Ouni Ecole de Technologie Superieure (ETS) | ||
14:00 90mTalk | Characterizing Self-Admitted Technical Debt Generated by AI Coding Agents Mining Challenge Zaki Brahmi ETS Montreal, University of Quebec, Ali Ouni Ecole de Technologie Superieure (ETS), Mohammed Sayagh ETS Montreal, University of Quebec, Mohamed Aymen saied Laval University | ||
14:00 90mTalk | How Do Agents Perform Code Optimization? An Empirical Study Mining Challenge Huiyun Peng Purdue University, Antonio Zhong Qiu Purdue University, Ricardo Andres Calvo Mendez Purdue University, Kelechi G. Kalu Purdue University, James C. Davis Purdue University Pre-print | ||
14:00 90mTalk | Comparing AI Coding Agents: A Task-Stratified Analysis of Pull Request Acceptance Mining Challenge Giovanni Pinna University of Trieste, Jingzhi Gong King's College London, David Williams University College London, Federica Sarro University College London | ||
14:00 90mTalk | More Code, Less Reuse: Investigation on Code Quality and Reviewer Sentiment towards AI-generated Pull Requests Mining Challenge Haoming Huang Institute of Science Tokyo, Pongchai Jaisri Nara Institute of Science and Technology, Shota Shimizu Ritsumeikan University, Lingfeng Chen Kyushu University, Sota Nakashima Kyushu University, Gema Rodríguez-Pérez Department of Computer Science, Mathematics, Physics and Statistics, University of British Columbia, Okanagan Campus DOI Pre-print | ||
14:00 90mShort-paper | On the Adoption of AI Coding Agents in Open-source Android and iOS Development Mining Challenge Muhammad Ahmad Khan Lahore University of Management Sciences, Hasnain Ali Lahore University of Management Sciences, Muneeb Rana Xtra App Studios, Muhammad Saqib Ilyas Lahore University of Management Sciences, Abdul Ali Bangash Lahore University of Management Sciences Pre-print | ||
14:00 90mTalk | Who Writes the Docs in SE 3.0? Agent vs. Human Documentation Pull Requests Mining Challenge Kazuma Yamasaki Nara Institute of Science and Technology, Joseph Ayobami Joshua Nara Institute of Science and Technology, Tasha Settewong Nara Institute of Science and Technology, Mahmoud Alfadel University of Calgary, Kazumasa Shimari Nara Institute of Science and Technology, Kenichi Matsumoto Nara Institute of Science and Technology DOI Pre-print | ||
14:00 90mTalk | Testing with AI Agents: An Empirical Study of Test Generation Frequency, Quality, and Coverage Mining Challenge Suzuka Yoshimoto NARA Institute of Science and Technology, Shun Fujita NARA Institute of Science and Technology, Kosei Horikawa , Daniel Feitosa University of Groningen, Yutaro Kashiwa Nara Institute of Science and Technology, Hajimu Iida Nara Institute of Science and Technology | ||
14:00 90mTalk | Safer Builders, Risky Maintainers: A Comparative Study of Breaking Changes in Human vs Agentic PRs Mining Challenge K M Ferdous Kennesaw State University, Dipayan Banik Quanta Technology, Kowshik Chowdhury Kennesaw State University, Shazibul Islam Shamim Kennesaw State University | ||
14:00 90mTalk | On the Reliability of Agentic AI in Continuous Integration Pipelines Mining Challenge Jasem Khelifi École de technologie supérieure, Mahi Begoug ETS Montreal, Ali Ouni Ecole de Technologie Superieure (ETS), Mohammed Sayagh ETS Montreal, University of Quebec, Mohamed Aymen saied Laval University, Moataz Chouchen Concordia University | ||
14:00 90mTalk | Early-Stage Prediction of Review Effort in AI-Generated Pull Requests Mining Challenge Dao Sy Duy Minh University of Science - VNUHCM, Huynh Trung Kiet University of Science - VNUHCM, Nguyen Lam Phu Quy University of Science - VNUHCM, Pham Phu Hoa University of Science - VNUHCM, Tran Chi Nguyen University of Science - VNUHCM, Nguyen Dinh Ha Duong University of Science - VNUHCM, Truong Bao Tran University of Economics and Law -VNUHCM | ||
14:00 90mTalk | Test Coverage of Code Changes in AI-Generated Pull Requests Mining Challenge Tales Alves Informatics Center, Federal University of Pernambuco, Leopoldo Teixeira Federal University of Pernambuco | ||
14:00 90mTalk | When AI Teammates Meet Code Review: Collaboration Signals Shaping the Integration of Agent-Authored Pull Requests Mining Challenge Pre-print | ||
14:00 90mTalk | Toward Instructions-as-Code: Understanding the Impact of Instruction Files on Agentic Pull Requests Mining Challenge | ||
14:00 90mTalk | An Empirical Study of Code Clone Genealogies in Human–AI Collaborative Development Mining Challenge Denis Sousa State University of Ceara, Brazil, Italo Uchoa State University of Ceará, Matheus Paixao State University of Ceará, Chaiyong Rakhitwetsagul Mahidol University, Thailand, Thiago Lima State University of Ceara, Brazil | ||
14:00 90mTalk | On the Footprints of Reviewer Bots' Feedback on Agentic Pull Requests in OSS GitHub Repositories Mining Challenge Syeda Kaneez Fatima Lahore University of Management Sciences, Yousuf Abrar Lahore University of Management Sciences, Abdul Rehman Lahore University of Management Sciences, Amelia Nawaz Lahore University of Management Sciences, Shamsa Abid National University of Computer and Emerging Sciences, Abdul Ali Bangash Lahore University of Management Sciences | ||
14:00 90mTalk | When Bots Get the Boot: Understanding Pull Request Rejections in the Era of AI Coders Mining Challenge | ||
14:00 90mTalk | Understanding the Rejection of Fixes Generated by Agentic Pull Requests - Insights from the AIDev Dataset Mining Challenge Mahmoud Abujadallah ETS - Québec University, Ali Arabat ETS - Québec University, Mohammed Sayagh ETS Montreal, University of Quebec | ||
14:00 90mTalk | What to Cut? Predicting Unnecessary Methods in Agentic Code Generation Mining Challenge Kan Watanabe Nara Institute of Science and Technology, Tatsuya Shirai Nara Institute of Science and Technology, Yutaro Kashiwa Nara Institute of Science and Technology, Hajimu Iida Nara Institute of Science and Technology | ||
14:00 90mTalk | How AI Coding Agents Modify Code: A Large-Scale Study of GitHub Pull Requests Mining Challenge Daniel Ogenrwot University of Nevada Las Vegas, John Businge University of Antwerp; Flanders Make; University of Nevada at Las Vegas DOI Pre-print | ||
14:00 90mTalk | Reliability of AI Bots Footprints in GitHub Actions CI/CD Workflows Mining Challenge Syed Muhammad Ashhar Shah Lahore University of Management Sciences, Lahore, Sehrish Habib Lahore University of Management Sciences, Lahore, Muizz Ahmed Hussain Lahore University of Management Sciences, Lahore, Maryam Abdul Ghafoor Lahore University of Management Sciences, Lahore, Abdul Ali Bangash Lahore University of Management Sciences | ||
14:00 90mTalk | The Dose Makes the Agent: Therapeutic Index Analysis of AI Coding Contributions Mining Challenge Giuseppe Destefanis University College London, Ronnie de Souza Santos University of Calgary, Marco Ortu University of Cagliari, Mairieli Wessel Radboud University | ||
14:00 90mTalk | Beyond Bug Fixes: An Empirical Investigation of Post-Merge Code Quality Issues in Agent-Generated Pull Requests Mining Challenge Shamse Tasnim Cynthia University of Saskatchewan, Al Muttakin University of Saskatchewan, Banani Roy University of Saskatchewan | ||
14:00 90mTalk | Why Are Agentic Pull Requests Merged or Rejected? An Empirical Study Mining Challenge Sien Reeve O. Peralta Waseda University, Fumika Hoshi Waseda University, Hironori Washizaki Waseda University, Naoyasu Ubayashi Waseda University, Inase Kondo Osaka University, Yoshiki Higo Osaka University, Hiroki Mukai Ritsumeikan University, Norihiro Yoshida Ritsumeikan University, Kazuki Kusama , Hidetake Tanaka Nara Institute of Science and Technology, Youmei Fan Nara Institute of Science and Technology | ||
14:00 90mTalk | Let's Make Every Pull Request Meaningful: An Empirical Analysis of Developer and Agentic Pull Requests Mining Challenge Haruhiko Yoshioka Nara Institute of Science and Technology, Takahiro Monno Nara Institute of Science and Technology, Haruka Tokumasu Kyushu University, Taiki Wakamatsu Kyushu University, Yuki Ota Ritsumeikan University, Nimmi Weeraddana University of Calgary , Kenichi Matsumoto Nara Institute of Science and Technology DOI Pre-print | ||
14:00 90mTalk | Humans Integrate, Agents Fix: How Agent-Authored Pull Requests Are Referenced in Practice Mining Challenge Islem Khemissi Concordia University, Moataz Chouchen Concordia University, Dong Wang Tianjin University, Raula Gaikovina Kula The University of Osaka | ||
14:00 90mShort-paper | How Do Agentic AI Systems Deal With Software Energy Concerns? A Pull Request-Based Study Mining Challenge Tanjum Motin Mitul University of Manitoba, Md. Masud Mazumder University of Manitoba, Md Nahidul Islam Opu University of Manitoba, Shaiful Chowdhury University of Manitoba Pre-print | ||
14:00 90mTalk | When AI Writes Code: Investigating Security Issues in Agentic Software Changes Mining Challenge Esteban Dectot-Le Monnier de Gouville Polytechnique Montréal, Mohammad Hamdaqa Polytechnique Montreal, Moataz Chouchen Concordia University | ||
14:00 90mTalk | Novice Developers Produce Larger Review Overhead for Project Maintainers while Vibe Coding Mining Challenge Syed Ammar Asdaque Lahore University of Management Sciences, Imran Haider Lahore University of Management Sciences, Muhammad Umar Malik Lahore University of Management Sciences, Maryam Abdul Ghafoor Lahore University of Management Sciences, Lahore, Abdul Ali Bangash Lahore University of Management Sciences | ||
14:00 90mTalk | Code Change Characteristics and Description Alignment: A Comparative Study of Agentic versus Human Pull Requests Mining Challenge Pre-print | ||
14:00 90mTalk | A Task-Level Evaluation of AI Agents in Open-Source Projects Mining Challenge Shojibur Rahman Idaho State University, Md Fazle Rabbi Idaho State University, Minhaz Zibran Idaho State University Pre-print | ||
14:00 90mTalk | Behind Agentic Pull Requests: An Empirical Study on Developer Interventions in AI Agent-Authored Pull Requests Mining Challenge Syrine Khelifi École de technologie supérieure (ÉTS) Montréal, Ali Ouni Ecole de Technologie Superieure (ETS), Maha Khemaja ISSAT Sousse, PRINCE Lab, University of Sousse | ||
14:00 90mTalk | Readability of AI-Generated Pull Request Descriptions Across Pull Request Types Mining Challenge Aidan Tobar Bowling Green State University, Joseph Peterson Bowling Green State University, Abbas Heydarnoori Bowling Green State University | ||
14:00 90mShort-paper | The Quiet Contributions: Insights into AI-Generated Silent Pull Requests Mining Challenge S. M. Mahedy Hasan Idaho State University, Md Fazle Rabbi Idaho State University, Minhaz Zibran Idaho State University Pre-print | ||
14:00 90mTalk | AI IDEs or Autonomous Agents? Measuring the Impact of Coding Agents on Software Development Mining Challenge Shyam Agarwal Carnegie Mellon University, Hao He Carnegie Mellon University, Bogdan Vasilescu Carnegie Mellon University Pre-print | ||
14:00 90mTalk | AI builds, We Analyze: An Empirical Study of AI-Generated Build Code Quality Mining Challenge | ||
14:00 90mTalk | Understanding Dominant Themes in Reviewing Agentic AI-authored Code Mining Challenge Md. Asif Haider University of California, Irvine, Thomas Zimmermann University of California, Irvine Pre-print | ||
14:00 90mTalk | Why and When Agentic Pull Requests are (not) Accepted: An Exploratory Study Mining Challenge Marius Christoph Strauss Anhalt University of Applied Sciences, Sandro Schulze Anhalt University of Applied Sciences DOI Pre-print | ||
14:00 90mTalk | Analyzing Message-Code Inconsistency in AI Coding Agent-Authored Pull Requests Mining Challenge Jingzhi Gong King's College London, Giovanni Pinna University of Trieste, Yixin Bian Harbin Normal University, Jie M. Zhang King's College London | ||
14:00 90mTalk | How Do Agentic AI Systems Address Performance Optimizations? A BERTopic-Based Analysis of Pull Requests Mining Challenge Md Nahidul Islam Opu University of Manitoba, Md Shahidul Islam University of Manitoba, Muhammad Asaduzzaman University of Windsor, Shaiful Chowdhury University of Manitoba Pre-print | ||
14:00 90mTalk | Mining Type Constructs Using Patterns in AI-Generated Code Mining Challenge Imgyeong Lee University of Alberta, Tayyib Ul Hassan University of Alberta, Abram Hindle University of Alberta | ||
14:00 90mTalk | Bug-Fixing in the Age of AI: Human vs. Agentic Pull Requests Mining Challenge Renato Domingues UFPE, Fernando Castor University of Twente, Fernanda Madeiral Universidade Federal de Pernambuco | ||
14:00 90mTalk | Why Are AI Agent–Involved Pull Requests (Fix-Related) Remain Unmerged? An Empirical Study Mining Challenge Khairul Alam University of Saskatchewan, Saikat Mondal University of Saskatchewan, Banani Roy University of Saskatchewan | ||
14:00 90mTalk | LGTM! Characteristics of Auto-Merged LLM-based Agentic PRs Mining Challenge Ruben Branco LASIGE, Informática, Faculdade de Ciências, Universidade de Lisboa, Paulo Canelas Carnegie Mellon University, Catarina Gamboa Carnegie Mellon University and University of Lisbon, Alcides Fonseca LASIGE; University of Lisbon DOI Pre-print Media Attached | ||
14:00 90mTalk | Do AI-Generated Pull Requests Get Rejected More? (Yes but Why?) Mining Challenge Rosie Wang University of Alberta, Zhou Yang University of Alberta, Alberta Machine Intelligence Institute | ||
14:00 90mTalk | How AI Coding Agents Communicate: A Study of Pull Request Characteristics and Human Review Responses Mining Challenge Kan Watanabe Nara Institute of Science and Technology, Rikuto Tsuchida Nara Institute of Science and Technology, Takahiro Monno Nara Institute of Science and Technology, Bin Huang Nara Institute of Science and Technology, Kazuma Yamasaki Nara Institute of Science and Technology, Youmei Fan Nara Institute of Science and Technology, Kazumasa Shimari Nara Institute of Science and Technology, Kenichi Matsumoto Nara Institute of Science and Technology Pre-print | ||
14:00 90mTalk | Where Do AI Coding Agents Fail? An Empirical Study of Failed Agentic Pull Requests in GitHub Mining Challenge Ramtin Ehsani Drexel University, Sakshi Pathak Drexel University, Shriya Rawal Drexel University, Abdullah Al Mujahid Missouri University of Science and Technology, Mia Mohammad Imran Missouri University of Science and Technology, Preetha Chatterjee Drexel University, USA Pre-print | ||
14:00 90mTalk | An Empirical Study of Tests in Agentic Pull Requests Mining Challenge Sabrina Haque The University of Texas at Arlington, Sarvesh Ingale The University of Texas at Arlington, Christoph Csallner University of Texas at Arlington DOI Pre-print Media Attached | ||
14:00 90mTalk | Who Said CVE? How Vulnerability Identifiers Are Mentioned by Humans, Bots, and Agents in Pull Requests Mining Challenge Pien Rooijendijk Radboud University, Christoph Treude Singapore Management University, Mairieli Wessel Radboud University | ||
14:00 90mTalk | Behavioral Analysis of AI Code Generation Agents: Edit, Rewrite, and Repetition Mining Challenge Mahdieh Abazar University of Calgary, Reyhaneh Farahmand University of Calgary, Gouri Ginde Schulich School of Engineering, University of Calgary, Calgary, Alberta, Canada, Benjamin Tan University of Calgary, Lorenzo De Carli University of Calgary, Canada | ||
14:00 90mTalk | A Study on Code Clone Lifecycles in Pull Requests Created by AI Agents Mining Challenge Italo Uchoa State University of Ceará, Denis Sousa State University of Ceara, Brazil, Henrique Chuvas State University of Ceará, Matheus Paixao State University of Ceará, Chaiyong Rakhitwetsagul Mahidol University, Thailand, Thiago Lima State University of Ceara, Brazil | ||
14:00 90mTalk | On Autopilot? An Empirical Study of Human–AI Teaming and Review Practices in Open Source Mining Challenge Haoyu Gao The University of Melbourne, Peerachai Banyongrakkul The University of Melbourne, Hao Guan the University of Melbourne, Mansooreh Zahedi The Univeristy of Melbourne, Christoph Treude Singapore Management University | ||
14:00 90mTalk | From Industry Claims to Empirical Reality: An Empirical Study of Code Review Agents in Pull Requests Mining Challenge Kowshik Chowdhury Kennesaw State University, Dipayan Banik Quanta Technology, K M Ferdous Kennesaw State University, Shazibul Islam Shamim Kennesaw State University | ||
14:00 90mTalk | A Study of Library Usage in Agent-Authored Pull Requests Mining Challenge DOI Pre-print | ||
14:00 90mTalk | Studying the Footprints of AI Coding Agents in Blockchain Repositories Mining Challenge Munim Iftikhar Lahore University of Management Sciences, Lahore, Maaz Shahid Lahore University of Management Sciences, Lahore, Shahreyar Ashraf Lahore University of Management Sciences, Lahore, Muhammad Saqib Ilyas Lahore University of Management Sciences, Abdul Ali Bangash Lahore University of Management Sciences | ||
14:00 90mTalk | Human-Agent versus Human Pull Requests: A Testing-Focused Characterization and Comparison Mining Challenge Roberto Milanese Politecnico di Torino, University of Molise, Francesco Salzano University of Molise, Angelica Spina University of Molise, Antonio Vitale Politecnico di Torino, University of Molise, Remo Pareschi University of Molise, Fausto Fasano University of Molise, Mattia Fazzini University of Minnesota DOI Pre-print | ||
14:00 90mTalk | Do AI Agents Really Improve Code Readability? Mining Challenge Kyogo Horikawa National Institute of Technology, Nara College, Kosei Horikawa , Yutaro Kashiwa Nara Institute of Science and Technology, Hidetake Uwano National Institute of Technology, Nara College, Japan, Hajimu Iida Nara Institute of Science and Technology | ||
14:00 90mTalk | When is Generated Code Difficult to Comprehend? Assessing AI Agent Python Code Proficiency in the Wild Mining Challenge Nanthit Temkulkiat Mahidol University, Chaiyong Rakhitwetsagul Mahidol University, Thailand, Morakot Choetkiertikul Mahidol University, Thailand, Ruksit Rojpaisarnkit Nara Institute of Science and Technology, Raula Gaikovina Kula The University of Osaka | ||
14:00 90mTalk | How do Agents Refactor: An Empirical Study Mining Challenge Lukas Ottenhof University of Alberta, Daniel Penner University of Alberta, Abram Hindle University of Alberta, Thibaud Lutellier University of Alberta Pre-print | ||
14:00 90mTalk | An Empirical Analysis of Test Failures in AI-Generated Pull Requests Mining Challenge Alireza Hoseinpour Bowling Green State University, Sajjad Rezvani Boroujeni Bowling Green State University, Jashhvanth Tamilselvan Kunthavai Bowling Green State University, Kyle Cusimano Bowling Green State University, Abbas Heydarnoori Bowling Green State University | ||
16:00 - 17:30 | Session 3-B: Demo and ToolsData and Tool Showcase Track / MSR Program at Catering and Exhibition Hall (Europa I to IV) | ||
16:00 50mTalk | MOOT: a Repository of many Multi-objective Optimization Tasks Data and Tool Showcase Track Tim Menzies North Carolina State University, Tao Chen University of Birmingham, Yulong Ye University of Birmingham, Kishan Kumar Ganguly NC State, Amirali Rayegan NC State, Srinath Srinivasan North Carolina State University, Andre Lustosa North Carolina State University | ||
16:00 50mTalk | Mapping Decentralized Autonomous Organization Governance Across Chains: An Updated, Multi-Platform Dataset Data and Tool Showcase Track Mashiat Amin Farin University of Texas at Dallas, Samer Hassan Institute of Knowledge Technology, Universidad Complutense de Madrid, Madrid, Spain & Berkman Klein Center at Harvard University, Cambridge MA, USA, Javier Arroyo Dpt. of Computer Science, Universidad de Alcalá, Madrid, Spain & Institute of Knowledge Technology, Universidad Complutense de Madrid Madrid, Spain | ||
16:00 50mTalk | Assessing Task-based Chatbots: Snapshot and Curated Datasets for Dialogflow Data and Tool Showcase Track Elena Masserini University of Milano - Bicocca, Diego Clerissi University of Milano-Bicocca, Daniela Micucci University of Milano-Bicocca, Italy, Leonardo Mariani University of Milano-Bicocca | ||
16:00 50mTalk | LILA: Decentralized Build Reproducibility Monitoring for the Functional Package Management Model Data and Tool Showcase Track Julien Malka LTCI, Télécom Paris, Institut Polytechnique de Paris, France, Arnout Engelen Independent | ||
16:00 50mTalk | Mining Kubernetes Repositories: The Cloud was Not Built in a Day Data and Tool Showcase Track Giuseppe Destefanis University College London, Silvia Bartolucci University College London, Daniel Feitosa University of Groningen | ||
16:00 50mTalk | RustXec: A Vulnerability Reproduction Dataset for Assessing Security Risks in Open-Source Rust Applications Data and Tool Showcase Track Zhengjie Ji Virginia Tech, Xin Wang Virginia Tech, Wang Lingxiang Unaffiliated, Geng Li Wake Forest University, Fan Yang Wake Forest University, Ying Zhang Wake Forest University | ||
16:00 50mTalk | OSSGameBench: A Large-Scale Dataset of Development Activities in Open-Source Video Games Data and Tool Showcase Track DOI Pre-print | ||
16:00 50mTalk | JavaBackports: A Dataset for Benchmarking Automated Backporting in Java Data and Tool Showcase Track Kaushal Kahapola University of Moratuwa, Sri Lanka, Sharada Galappaththi University of Moratuwa, Sri Lanka, Dinith Ranasinghe University of Moratuwa, Sri Lanka, Ridwan Salihin Shariffdeen SonarSource, Nisansa de Silva University of Moratuwa, Sri Lanka, Srinath Perera WSO2, Sandareka Wickramanayake University of Moratuwa, Sri Lanka | ||
16:00 50mTalk | HackRep: A Large-Scale Dataset of GitHub Hackathon Projects Data and Tool Showcase Track Sjoerd Halmans Eindhoven University of Technology, Lavinia Francesca Paganini Eindhoven University of Technology, Alexander Serebrenik Eindhoven University of Technology, Alexander Nolte Eindhoven University of Technology | ||
16:00 50mTalk | KubeObjects: A Dataset of Real-World Kubernetes Objects Data and Tool Showcase Track Matteo Grella University of Twente, Danil Aliforenko University of Twente, Luca Mariot University of Twente | ||
16:00 50mTalk | IssuePilot: An Agentic Framework for Personalized Issue Recommendation and Onboarding in Open-Source Projects Data and Tool Showcase Track | ||
16:00 50mTalk | DBSecQA: A Curated Dataset of Developer Discussions on Database Security from Stack Exchange Data and Tool Showcase Track Md Rakibul Islam Lamar University, Farha Kamal Lamar University, MD HUMAUN KABIR Lamar University, Md Murad Sharif Lamar University | ||
16:00 50mTalk | PoolinGH: Fast, Efficient, and Robust GitHub Repository Mining Data and Tool Showcase Track Maxime ANDRÉ Namur Digital Institute, University of Namur, Marco Raglianti REVEAL @ Software Institute – USI, Lugano, Switzerland, Souhaila Serbout University of Zurich, Zurich, Switzerland, Anthony Cleve University of Namur, Michele Lanza Software Institute - USI, Lugano Pre-print | ||
16:00 50mTalk | GivenWhenThen: A Dataset of BDD Test Scenarios Mined from Open Source Projects Data and Tool Showcase Track Luciano Belo de Alcântara Júnior UFMG, João Eduardo Montandon Universidade Federal de Minas Gerais (UFMG) | ||
16:00 50mTalk | AnoMod: A Dataset for Anomaly Detection and Root Cause Analysis in Microservice System Data and Tool Showcase Track Ke Ping University of Helsinki, Hamza Bin Mazhar University of Helsinki, Yuqing Wang University of Helsinki, Finland, Ying Song University of Helsinki, Mika Mäntylä University of Helsinki and University of Oulu | ||
16:00 50mTalk | GLiSE: A Prompt-Driven and ML-Powered Tool for Automated Grey Literature Extraction in Software Engineering Data and Tool Showcase Track Brahim Mahmoudi École de technologie supérieure, Zacharie Chenail-Larcher École de technologie supérieure (ÉTS), Houcine Abdelkader Cherief Ecole de Technologie Supérieure, Quentin Stiévenart Université du Québec à Montréal, Naouel Moha École de Technologie Supérieure (ETS), Florent AVELLANEDA Université du Québec à Montréal | ||
16:00 50mTalk | OmniCCG: Agnostic Code Clone Genealogy Extractor Data and Tool Showcase Track Denis Sousa State University of Ceara, Brazil, Matheus Paixao State University of Ceará, Thiago Lima State University of Ceara, Brazil, Adriely Silva State University of Ceara, Brazil, Italo Uchoa State University of Ceará, Chaiyong Ragkhitwetsagul Mahidol University | ||
16:00 50mTalk | GitEvo: Code Evolution Analysis for Git Repositories Data and Tool Showcase Track Andre Hora UFMG Pre-print | ||
16:00 50mTalk | Skyt: Prompt Contracts for Software Repeatability in LLM-Assisted Development Data and Tool Showcase Track Heitor Roriz Filho Massimus, Nasser Jazdi University of Stuttgart, Vicente Lucena Universidade Federal do Amazonas | ||
16:00 50mTalk | InEx-Bug: A Human Annotated Dataset of Intrinsic and Extrinsic Bugs in the NPM Ecosystem Data and Tool Showcase Track Tanner Wright University of British Columbia, Adams Chen University of British Columbia, Gema Rodríguez-Pérez Department of Computer Science, Mathematics, Physics and Statistics, University of British Columbia, Okanagan Campus | ||
16:00 - 17:30 | |||
16:45 40mTalk | Running Large Language Models at Scale for Mining Software Repositories: Lessons Learned from HPC-Based Batch Inference Tutorials Ruoyu Su , Matteo Esposito University of Oulu, Davide Taibi University of Southern Denmark and University of Oulu, Valentina Lenarduzzi University of Southern Denmark | ||
Tue 14 AprDisplayed time zone: Brasilia, Distrito Federal, Brazil change
09:00 - 10:30 | |||
09:00 10mTalk | Day Opening MSR Program | ||
09:10 30mTalk | Vision 2: The State of Data Mining, Benchmarks, Double Blind Trials, and Software Engineering Systems in Industry MSR Program | ||
09:40 50mKeynote | From Hallucinations to Helpful Agents: Advancing Trustworthy Automation in Code Review Keynotes Patanamon Thongtanunam The University of Melbourne | ||
14:00 - 15:30 | |||
14:00 10mTalk | How are MLOps Frameworks Used in Open Source Projects? An Empirical Characterization Technical Papers Fiorella Zampetti University of Sannio, Italy, Federico Stocchetti University of Sannio, Italy, Federica Razzano University of Sannio, Italy, Damian Andrew Tamburri University of Sannio - JADS/NXP Semiconductors, Massimiliano Di Penta University of Sannio, Italy Pre-print | ||
14:10 10mTalk | Do We Agree on What an “Audit” Is? Toward Standardized Smart Contract Audit Reporting Technical Papers Ilham Qasse Reykjavik University, Mohammad Hamdaqa Polytechnique Montreal, Gísli Hjálmtýsson Reykjavik University | ||
14:20 10mTalk | AFGNN: API Misuse Detection using Graph Neural Networks and Clustering Technical Papers Ponnampalam Pirapuraj IIT Hyderabad, Tamal Mondal Oracle, Sharanya Gupta Yokogawa Digital, Akash Lal Microsoft Research, Somak Aditya IIT Kharagpur, Jyothi Vedurada IIT Hyderabad | ||
14:30 10mTalk | An Empirical Analysis of Cross-OS Portability Issues in Python Projects Technical Papers Denini Silva Federal University of Pernambuco, MohamadAli Farahat North Carolina State University, Marcelo d'Amorim North Carolina State University Pre-print | ||
14:40 10mTalk | Learning Compiler Fuzzing Mutators from Historical Bugs Technical Papers Lingjun Liu North Carolina State University, Feiran Qin North Carolina State University, Owolabi Legunsen Cornell University, Marcelo d'Amorim North Carolina State University | ||
14:50 40mMeeting | Mining Challenge Finalists MSR Program | ||
14:00 - 15:30 | |||
14:00 10mTalk | Analyzing GitHub Issues and Pull Requests in nf-core Pipelines: Insights into nf-core Pipeline Repositories Technical Papers | ||
14:10 10mTalk | Modeling Sampling Workflows for Code Repositories Technical Papers Romain Lefeuvre University of Rennes, Maiwenn Le Goasteller University of Rennes, Inria, CNRS, IRISA, Jessie Galasso-Carbonnel McGill University, Benoit Combemale University of Rennes, Inria, CNRS, IRISA, Quentin Perez INSA Rennes, Houari Sahraoui DIRO, Université de Montréal | ||
14:20 10mTalk | Quantifying Competitive Relationships Among Open-Source Software Projects Technical Papers Yuki Takei Japan Advanced Institute of Science and Technology, Toshiaki Aoki JAIST, Chaiyong Rakhitwetsagul Mahidol University, Thailand Pre-print | ||
14:30 10mTalk | Role of CI Adoption in Mobile App Success: An Empirical Study of Open-Source Android Projects Technical Papers xiaoxin zhou University of Toronto, Taher A. Ghaleb Trent University, Safwat Hassan University of Toronto Pre-print | ||
14:40 10mTalk | ML in a Box: Analyzing Containerization Practices in Open Source ML Projects Technical Papers Faten Jebari Grand Valley State University, Emna Ksontini University of North Carolina Wilmington, Amine Barrak Oakland University, USA, Wael Kessentini DePaul University | ||
14:50 10mTalk | An Empirical Study of Policy as Code: Adoption, Purpose, and Maintenance Technical Papers Ruben Opdebeeck Vrije Universiteit Brussel, Mahmoud Alfadel University of Calgary, Akond Rahman Auburn University, Yutaro Kashiwa Nara Institute of Science and Technology, João F. Ferreira Faculty of Engineering, University of Porto & INESC-ID, Raula Gaikovina Kula The University of Osaka, Coen De Roover Vrije Universiteit Brussel Pre-print | ||
15:00 10mTalk | Tracing Stereotypes in Pre-trained Transformers: From Biased Neurons to Fairer Models Technical Papers Gianmario Voria University of Salerno, Moses Openja Polytechnique Montreal, Foutse Khomh Polytechnique Montréal, Gemma Catolino University of Salerno, Fabio Palomba University of Salerno Pre-print | ||
15:10 5mIndustry talk | Can Data Mining Help to Survive the Annual Compiler Upgrade? Industry Track Gunnar Kudrjavets Amazon Web Services, USA, Aditya Kumar Google, Piotr Przymus Nicolaus Copernicus University in Toruń, Poland Pre-print | ||
15:15 5mTalk | Underutilization in Research GPU Clusters: SE Challenges Industry Track Krzysztof Kaczmarski Warsaw University of Technology, Jakub Narębski Nicolaus Copernicus University in Toruń, Piotr Przymus Nicolaus Copernicus University in Toruń, Poland | ||
16:00 - 18:00 | |||
16:00 30mTalk | Vision: TBA MSR Program | ||
16:30 20mAwards | FCA Award Winner MSR Program | ||
16:50 20mTalk | MIP 2016 Presentation MSR Program | ||
17:10 20mTalk | Closing Remarks MSR Program | ||
17:30 10mTalk | Invitation to MSR 2027 MSR Program | ||
Accepted Papers
Call for Registered Reports
The Springer Journal of Empirical Software Engineering (EMSE), in conjunction with the Mining Software Repositories (MSR) conference, is continuing the Registered Reports (RR) track. The RR track of MSR 2026 has two goals:
- Providing early feedback to authors in their initial study design. For papers submitted to the RR track, methods and proposed analyses are reviewed before execution.
- To prevent HARKing (hypothesizing after the results are known) for empirical studies.
Pre-registered studies follow a two-step process:
- Stage 1: Authors submit a report that describes a study they plan to undertake. The submitted report is evaluated by the reviewers of the RR track of MSR 2026. If accepted, authors of accepted pre-registered studies will be given the opportunity to present their report at MSR.
- Stage 2: Once a report has passed Stage 1, the authors conduct the study (i.e., the actual data collection, experiments, and analysis will take place) and they prepare a full paper based on the original plan and obtained results (which may also be negative) to be submitted for review to EMSE.
Type of Study
The RR track of MSR 2026 supports two types of papers:
Confirmatory Study: The researcher has a fixed hypothesis (or several fixed hypotheses) and the objective of the study is to find out whether the hypothesis is supported by the facts/data. An example of a completed confirmatory study:
- Inozemtseva, L., & Holmes, R. (2014, May). Coverage is not strongly correlated with test suite effectiveness. In Proceedings of the 36th International Conference on Software Engineering (pp. 435-445).
Exploratory Study: The researcher does not have a hypothesis (or has one that may change during the study). Often, the objective of such a study is to understand what is observed and answer questions such as WHY, HOW, WHAT, WHO, or WHEN. We include in this category registrations for which the researcher has an initial proposed solution for an automated approach (e.g., a new deep-learning-based defect prediction approach) that serves as a starting point for his/her exploration to reach an effective solution. Examples of completed exploratory studies:
- Gousios, G., Pinzger, M., & Deursen, A. V. (2014, May). An exploratory study of the pull-based software development model. In Proceedings of the 36th International Conference on Software Engineering (pp. 345-355).
- Rodrigues, I. M., Aloise, D., Fernandes, E. R., & Dagenais, M. (2020, June). A Soft Alignment Model for Bug Deduplication. In Proceedings of the 17th International Conference on Mining Software Repositories (pp. 43-53).
Evaluation Criteria and Possible Outcomes
The RR PC members will review papers in both Stage 1 and Stage 2. Four PC members will review the Stage 1 submission, and three will review the Stage 2 submission. The reviewers will evaluate RR track submissions based on the following criteria:
- The relevance to MSR (out of scope registrations that do no align with the venue’s topics will be desk rejected).
- The importance of the research question(s).
- The logic, rationale, and plausibility of the proposed hypotheses.
- The soundness and feasibility of the methodology and analysis pipeline (including statistical power analysis where appropriate).
- (For a confirmatory study) Whether the clarity and degree of methodological detail is sufficient to exactly replicate the proposed experimental procedures and analysis pipeline.
- (For a confirmatory study) Whether the authors have pre-specified sufficient outcome-neutral tests for ensuring that the results obtained can test the stated hypotheses, including positive controls and quality checks.
- (For exploratory study, if applicable) The description of the data set that is the base for exploration.
The outcome of the RR report review is one of the following:
- In-Principle Acceptance (IPA): The reviewers agree that the study is relevant, the outcome of the study (whether confirmation or rejection of the hypothesis) is of interest to the community, the protocol for data collection is sound, and the analysis methods are adequate. The authors can engage in the actual study for Stage 2. If the protocol is adhered to (or deviations are thoroughly justified), the study is likely to be published. Of course, this being a journal submission, a revision of the submitted manuscript may be necessary. Reviewers will especially evaluate how precisely the protocol of the accepted pre-registered report is followed, or whether deviations are well-justified.
- Continuity Acceptance (CA): The reviewers agree that the study is relevant and the (initial) methods appear to be appropriate. However, for exploratory studies, implementation details and post-experiment analyses or discussions (e.g., why the proposed automated approach does not work) may require follow-up revisions. We will do our best to assign the same reviewers to Stages 1 and 2.
- Rejection: The reviewers do not agree on the relevance of the study or are not convinced that the study design is sufficiently mature. Comments are provided to the authors to improve the study design before starting it.
- Note: For MSR 2026, only confirmatory studies are granted an IPA. An exploratory study in software engineering often cannot be adequately assessed until after the study has been completed and the findings are elaborated and discussed in a full paper. For example, consider a study in an RR proposing defect prediction using a new deep learning architecture. This work falls under the exploratory category. It is difficult to offer IPA, as we do not know whether it is any better than a traditional approach based on, e.g., decision trees. Negative results are welcome; however, the negative results paper must go beyond presenting “we tried and failed”, but rather provide interesting insights to readers, e.g., why the results are negative or what that means for further studies on this topic (following criteria of REplication and Negative Results (RENE) tracks, e.g., https://saner2023.must.edu.mo/negativerestrack). Furthermore, it is important to note that authors are required to document all deviations (if any) in a section of the paper.
Key Dates
The timeline for the MSR 2026 RR track will be as follows:
-
November 14, 2025: Authors submit an abstract of their initial report.
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November 21, 2025: Authors submit their initial report.
- December 23, 2025: Authors receive PC members’ reviews.
- January 16, 2026: Authors submit a response letter + revised report in a single PDF.
- The response letter should address reviewers’ comments and questions.
- Combined, the length of the response letter + revised report must not exceed 12 pages (plus 1 additional page of references).
- The response letter does not need to follow ACM formatting instructions.
February 4, 2026January 31, 2026 [Updated]: Notification of Stage 1- Outcome: in-principal acceptance, continuity acceptance, or rejection.
- Before February 26, 2026: Authors submit their accepted RR report to any preprint hosting platform, such as OSF Preprints (https://osf.io/preprints) and SSRN (https://www.ssrn.com), as long as it assigns a publicly accessible DOI.
- To be checked by PC members for Stage 2
- Note: Due to the timeline, RRs will not be published in the MSR 2026 proceedings, but the authors of accepted reports will be required to present their reports at MSR 2026.
- Before September 28, 2026: Authors submit a full paper to EMSE. Instructions will be provided later. However, the following constraints will be enforced:
- Justifications must be given for any change of authors. If the authors are added/removed or the author order is changed between the original Stage 1 and the EMSE submission, all authors will need to complete and sign a “Change of authorship request form”. The Editors-in-Chief of EMSE and chairs of the RR track reserve the right to deny author changes. If you anticipate any authorship changes, please reach out to the chairs of the RR track as early as possible.
- PC members who reviewed a RR in Stage 1 and their directly supervised students cannot be added as authors of the corresponding submission in Stage 2.
Submission Process
Registered report submissions must not exceed 6 pages (plus 1 additional page of references). All submissions must be in PDF. The page limit is strict. Submissions must conform to the ACM double-column template, specified in the ACM Conference Proceedings Formatting Guidelines. Submissions must strictly conform to the ACM conference proceedings formatting instructions. Alterations of spacing, font size, and other changes that deviate from the instructions may result in desk rejection without further review.
Submissions can be made via the submission site (https://msr2026-rr.hotcrp.com) by the submission deadline. Any submission that does not comply with the instructions above and the mandatory information specified in the Author Guide is likely to be desk rejected.
Publication and Presentation
A full registration and in-person presentation are required for papers accepted at the conference.
Submission Policies
By submitting, the authors acknowledge that they are aware of and agree to be bound by the following policies: The ACM Policy and Procedures on Plagiarism and the IEEE Plagiarism FAQ. In particular, papers submitted to MSR 2026 must not have been published elsewhere and must not be under review or submitted for review elsewhere whilst under consideration for MSR 2026. Contravention of this concurrent submission policy will be deemed a serious breach of scientific ethics, and appropriate action will be taken in all such cases (including immediate rejection and reporting of the incident to ACM/IEEE). To check for double submission and plagiarism issues, the chairs reserve the right to (1) share the list of submissions with the PC Chairs of other conferences with overlapping review periods and (2) use external plagiarism detection software, under contract to the ACM or IEEE, to detect violations of these policies.
By submitting to MSR 2026, authors acknowledge that they conform to the authorship policy of the ACM and the authorship policy of the IEEE. This includes the following points related to the use of Generative AI:
“Generative AI tools and technologies, such as ChatGPT, may not be listed as authors of an ACM-published Work. The use of generative AI tools and technologies to create content is permitted but must be fully disclosed in the Work. For example, the authors could include the following statement in the Acknowledgements section of the Work: ChatGPT was utilized to generate sections of this Work, including text, tables, graphs, code, data, citations, etc.). If you are uncertain about the need to disclose the use of a particular tool, err on the side of caution, and include a disclosure in the acknowledgments section of the Work.” - ACM
“The use of artificial intelligence (AI)–generated text in an article shall be disclosed in the acknowledgments section of any paper submitted to an IEEE Conference or Periodical. The sections of the paper that use AI-generated text shall have a citation to the AI system used to generate the text.” - IEEE
“If you are using generative AI software tools to edit and improve the quality of your existing text in much the same way you would use a typing assistant like Grammarly to improve spelling, grammar, punctuation, clarity, engagement or to use a basic word processing system to correct spelling or grammar, it is not necessary to disclose such usage of these tools in your Work.” - ACM.