| 2026 | Role of CI Adoption in Mobile App Success: An Empirical Study of Open-Source Android Projects. | Xiaoxin Zhou, Taher A. Ghaleb, Safwat Hassan |
| 2026 | Analyzing Dependency Distribution Changes Arising from Code Smell Interactions. | Zushuai Zhang, Elliott Wen, Ewan D. Tempero |
| 2026 | An Empirical Study on Deep Learning-based Line-Level Software Defect Prediction. | Enci Zhang, Yutong Jiang, Tianmeng Zhang, Haonan Tong |
| 2026 | How are MLOps Frameworks Used in Open Source Projects? An Empirical Characterization. | Fiorella Zampetti, Federico Stocchetti, Federica Razzano, Damian Andrew Tamburri, Massimiliano Di Penta |
| 2026 | Let's Make Every Pull Request Meaningful: An Empirical Analysis of Developer and Agentic Pull Requests. | Haruhiko Yoshioka, Takahiro Monno, Haruka Tokumasu, Taiki Wakamatsu, Yuki Ota, Nimmi Weeraddana, Kenichi Matsumoto |
| 2026 | Testing with AI Agents: An Empirical Study of Test Generation Frequency, Quality, and Coverage. | Suzuka Yoshimoto, Shun Fujita, Kosei Horikawa, Daniel Feitosa, Yutaro Kashiwa, Hajimu Iida |
| 2026 | Who Writes the Docs in SE 3.0?: Agent vs. Human Documentation Pull Requests. | Kazuma Yamasaki, Joseph Ayobami Joshua, Tasha Settewong, Mahmoud Alfadel, Kazumasa Shimari, Kenichi Matsumoto |
| 2026 | InEx-Bug: A Human Annotated Dataset of Intrinsic and Extrinsic Bugs in the NPM Ecosystem. | Tanner Wright, Adams Chen, Gema Rodrguez-Prez |
| 2026 | How AI Coding Agents Communicate: A Study of Pull Request Characteristics and Human Review Responses. | Kan Watanabe, Rikuto Tsuchida, Takahiro Monno, Bin Huang, Kazuma Yamasaki, Youmei Fan, Kazumasa Shimari, Kenichi Matsumoto |
| 2026 | What to Cut? Predicting Unnecessary Methods in Agentic Code Generation. | Kan Watanabe, Tatsuya Shirai, Yutaro Kashiwa, Hajimu Iida |
| 2026 | World of Logs: A Dataset of Logs from Online Documents. | Xiaohui Wang, Kundi Yao, Lizhi Liao, Pengyu Nie, Xuan Zhang, Weiyi Shang |
| 2026 | Do AI-Generated Pull Requests Get Rejected More? (Yes but Why?). | Yiru Wang, Zhou Yang |
| 2026 | Tracing Stereotypes in Pre-trained Transformers: From Biased Neurons to Fairer Models. | Gianmario Voria, Moses Openja, Foutse Khomh, Gemma Catolino, Fabio Palomba |
| 2026 | A Study on Code Clone Lifecycles in Pull Requests Created by AI Agents. | Italo Uchoa, Denis Sousa, Henrique Chuvas, Matheus Paixo, Chaiyong Ragkhitwetsagul, Thiago Lima Matos |
| 2026 | A Study of Library Usage in Agent-Authored Pull Requests. | Lukas Twist, Jie M. Zhang |
| 2026 | Combining Example-Based and Rule-Based Program Transformations to Resolve Build Conflicts. | Sheikh Shadab Towqir, Fei He, Todd Mytkowicz, Na Meng |
| 2026 | A Large-Scale Dataset of MCP Implementations on GitHub. | Benny Toeppe, Amine Barrak, Emna Ksontini |
| 2026 | Readability of AI-Generated Pull Request Descriptions Across Pull Request Types. | Aidan Tobar, Joseph Peterson, Abbas Heydarnoori |
| 2026 | When is Generated Code Difficult to Comprehend? Assessing AI Agent Python Code Proficiency in the Wild. | Nanthit Temkulkiat, Chaiyong Ragkhitwetsagul, Morakot Choetkiertikul, Ruksit Rojpaisarnkit, Raula Gaikovina Kula |
| 2026 | A Blueprint for Trustworthy Code Annotation at Scale: An LLM-Powered Pipeline for Industrial Software Analytics. | Ailon dos Santos Teixeira, Jaine Brito da Silva da Silva, Nikolas Rocha da Silva de Medeiros, Raimundo da Silva Barreto, Jos Reginaldo Hughes da Silva Carvalho, Alex Fernando da Silva Monteiro |
| 2026 | From Logic to Toolchains: An Empirical Study of Bugs in the TypeScript Ecosystem. | TianYi Tang, Saba Alimadadi, Nick Sumner |
| 2026 | Quantifying Competitive Relationships Among Open-Source Software Projects. | Yuki Takei, Toshiaki Aoki, Chaiyong Ragkhitwetsagul |
| 2026 | Running Large Language Models at Scale for Mining Software Repositories: Lessons Learned from HPC-Based Batch Inference. | Ruoyu Su, Matteo Esposito, Davide Taibi, Valentina Lenarduzzi |
| 2026 | Why and When Agentic Pull Requests are (not) Accepted: An Exploratory Study. | Marius Christoph Strauss, Sandro Schulze |
| 2026 | An Empirical Study of Code Clone Genealogies in Human-AI Collaborative Development. | Denis Sousa, Italo Uchoa, Matheus Paixo, Chaiyong Ragkhitwetsagul, Thiago Lima Matos |