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FORGE 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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