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 |