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ORCIDFORGE 2024: Lisbon, Portugal
- David Lo, Xin Xia, Massimiliano Di Penta, Xing Hu:
Proceedings of the 2024 IEEE/ACM First International Conference on AI Foundation Models and Software Engineering, FORGE 2024, Lisbon, Portugal, 14 April 2024. ACM 2024 - Hailong Wang
, Tongtong Xu, Bei Wang:
Deep Multiple Assertions Generation. 1-11 - Guanyu Wang, Yuekang Li, Yi Liu, Gelei Deng, Tianlin Li, Guosheng Xu, Yang Liu, Haoyu Wang, Kailong Wang:
MeTMaP: Metamorphic Testing for Detecting False Vector Matching Problems in LLM Augmented Generation. 12-23 - Hridya Dhulipala, Aashish Yadavally, Tien N. Nguyen:
Planning to Guide LLM for Code Coverage Prediction. 24-34 - Ashwin Prasad Shivarpatna Venkatesh, Samkutty Sabu, Amir M. Mir, Sofia Reis, Eric Bodden:
The Emergence of Large Language Models in Static Analysis: A First Look through Micro-Benchmarks. 35-39 - Jiahui Wu, Chengjie Lu, Aitor Arrieta, Tao Yue, Shaukat Ali:
Reality Bites: Assessing the Realism of Driving Scenarios with Large Language Models. 40-51 - Kimya Khakzad Shahandashti, Mithila Sivakumar, Mohammad Mahdi Mohajer, Alvine Boaye Belle, Song Wang, Timothy Lethbridge:
Assessing the Impact of GPT-4 Turbo in Generating Defeaters for Assurance Cases. 52-56 - Marcos Macedo, Yuan Tian, Filipe Roseiro Côgo, Bram Adams:
Exploring the Impact of the Output Format on the Evaluation of Large Language Models for Code Translation. 57-68 - Gianmario Voria, Gemma Catolino, Fabio Palomba:
Is Attention All You Need? Toward a Conceptual Model for Social Awareness in Large Language Models. 69-73 - Jonathan Katzy, Razvan Mihai Popescu, Arie van Deursen, Maliheh Izadi:
An Exploratory Investigation into Code License Infringements in Large Language Model Training Datasets. 74-85 - Junjie Li, Aseem Sangalay, Cheng Cheng, Yuan Tian, Jinqiu Yang:
Fine Tuning Large Language Model for Secure Code Generation. 86-90 - Tim van Dam, Frank van der Heijden, Philippe de Bekker, Berend Nieuwschepen, Marc Otten, Maliheh Izadi:
Investigating the Performance of Language Models for Completing Code in Functional Programming Languages: a Haskell Case Study. 91-102 - Changan Niu, Ting Zhang, Chuanyi Li, Bin Luo, Vincent Ng:
On Evaluating the Efficiency of Source Code Generated by LLMs. 103-107 - Seif Abukhalaf, Mohammad Hamdaqa, Foutse Khomh:
PathOCL: Path-Based Prompt Augmentation for OCL Generation with GPT-4. 108-118 - Scott Blyth, Christoph Treude, Markus Wagner:
Creative and Correct: Requesting Diverse Code Solutions from AI Foundation Models. 119-123 - Yifan Wu, Ying Li, Siyu Yu:
Commit Message Generation via ChatGPT: How Far Are We? 124-129
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