| 1 (S)M-Tu | MSR | | Refactoring for Dockerfile Quality: A Dive into Developer Practices and Automation Potential |
| 2 (S)M-Tu | MSR | Bikash Saha, Nanda Rani, Sandeep Kumar Shukla | MaLAware: Automating the Comprehension of Malicious Software Behaviours using Large Language Models (LLMs) |
| 3 (S)M-Tu | MSR | Md Shamimur Rahman, Zadia Codabux and Chanchal K. Roy | Investigating the Understandability of Review Comments on Code Change Requests |
| 4 (S)M-Tu | MSR | Daniele Bifolco, Pietro Cassieri, Giuseppe Scanniello, Massimiliano Di Penta, Fiorella Zampetti | Do LLMs Provide Links to Code Similar to what they Generate? A Study with Gemini and Bing CoPilot |
| 5 (S)M-Tu | MSR | Ahmed Adnan, Antu Saha, Oscar Chaparro | SPRINT: An Assistant for Issue Report Management |
| 6 (S)M-Tu | MSR | Md Fazle Rabbi, Arifa Islam Champa, Rajshakhar Paul, and Minhaz F. Zibran | Chasing the Clock: How Fast Are Vulnerabilities Fixed in the Maven Ecosystem? |
| 7 (S)M-Tu | MSR | Imen Joaua, Oussama Ben Sghaier, Houari Sahraoui | Combining Large Language Models with Static Analyzers for Code Review Generation |
| 8 (S)M-Tu | MSR | Youness Hourri, Alexandre Decan, Tom Mens | A Dataset of Contributor Activities in the NumFocus Open-Source Community |
| 9 (S)M-Tu | MSR | Piotr Przymus, Mikołaj Fejzer, Jakub Narębski, Radosław Woźniak, Łukasz Halada, Aleksander Kazecki, Mykhailo Molchanov, Krzysztof Stencel | HaPy-Bug - Human Annotated Python Bug Resolution Dataset |
| 10 (S)M-Tu | MSR | Piotr Przymus, Mikołaj Fejzer, Jakub Narębski, Krzysztof Rykaczewski, Krzysztof Stencel | Out of Sight, Still at Risk: The Lifecycle of Transitive Vulnerabilities in Maven |
| 11 (S)M-Tu | MSR | Nkiru Ede, Jens Dietrich, Uli Zuelicke | Popularity and Innovation in Maven Central |
| 12 (S)M-Tu | MSR | Baltasar Berretta, Augustus Thomas, Heather Guarnera | Dependency Update Adoption Patterns in the Maven Software Ecosystem |
| 13 (S)M-Tu | MSR | Mina Shehata, Saidmakhmud Makhkamjonoov, Mahad Syed, Esteban Parra | Cascading Effects: Analyzing Project Failure Impact in the Maven Central Ecosystem |
| 14 (S)M-Tu | MSR | Christoph Bühler, David Spielmann, Roland Meier, Guido Salvaneschi | TerraDS: A Dataset for Terraform HCL Programs |
| 15 (S)M-Tu | MSR | Rio Kishimoto, Tetsuya Kanda, Yuki Manabe, Katsuro Inoue, Shi Qiu, Yoshiki Higo | A Dataset of Software Bill of Materials for Evaluating SBOM Consumption Tools |
| 16 (S)M-Tu | MSR | Toufique Ahmed, Premkumar Devenu, Christoph Treude, Michael Pradel | Can LLMs Replace Manual Annotation of Software Engineering Artifacts? |
| 17 (S)M-Tu | MSR | Chavhan Sujeet Yashavant, Mitrajsinh Chavda, Saurabh Kumar, Amey Karkare, Angshuman Karmakar | SCRUBD: Smart Contracts Reentrancy and Unhandled Exceptions Vulnerability Dataset |
| 18 (S)M-Tu | MSR | Julien Malka, Stefano Zacchiroli, Théo Zimmermann | Does Functional Package Management Enable Reproducible Builds at Scale? Yes. |
| 31 S-M(Tu) | Forge | Zhimin Zhao | SE Arena: An Interactive Platform for Evaluating Foundation Models in Software Engineering |
| 32 S-M(Tu) | Forge | Aneri Gandhi | Automated Codebase Reconciliation using Large Language Models |
| 33 S-M(Tu) | Forge | Lola Solovyeva | AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code |
| 34 S-M(Tu) | Forge | Domenico Cotroneo | PyResBugs: A Dataset of Residual Python Bugs for Natural Language-Driven Fault Injection |
| 35 S-M(Tu) | Forge | Jonathan Katzy | The Heap: A Contamination-Free Multilingual Code Dataset for Evaluating Large Language Models |
| 36 S-M(Tu) | Forge | Ivan Petrukha | SwiftEval: Developing a Language-Specific Benchmark for LLM-generated Code Evaluation |
| 37 S-M(Tu) | Forge | Yevhenii Peteliev | Towards Generating App Feature Descriptions Automatically with LLMs: the Setapp Case Study |
| 62 S-M(Tu) | CAIN | Benjamin Weigell, Fabian Stieler,Bernhard Bauer | All You Need is an AI Platform: A Proposal for a Complete Reference Architecture |
| 63 S-M(Tu) | CAIN | Katherine R. Dearstyne, Pedro Antonio Alarcon Granadeno, Theodore Chambers, Jane Cleland-Huang | Evaluating Reinforcement Learning Safety in Cyber-Physical Systems |
| 64 S-M(Tu) | CAIN | Toufique Ahmed, Amin Alipour, Aftab Hussain, Toufique Ahmed, Stephen Huang, Md Rafiqul Islam Rabin, Bowen Xu | Finding Trojan Triggers in Code LLMs: An Occlusion-based Human-in-the-loop Approach |
| 65 S-M(Tu) | CAIN | Hadiza Yusuf, Khouloud Gaaloul | Navigating the Shift: Architectural Transformations and Emerging Verification Demands in AI-Enabled Cyber-Physical Systems |
| 66 S-M(Tu) | CAIN | | Random Perturbation Attacks on LLMs for Code Generatio |
| 67 S-M(Tu) | CAIN | | Safeguarding LLM-Applications: Specify or Train? |
| 68 S-M(Tu) | CAIN | | Task decomposition and RAG as Design Patterns for GenAI-based Systems: The case of Workflow Generation |