Fine Tuning Large Language Model for Secure Code GenerationNew Idea Paper
AI pair programmers, such as GitHub’s Copilot, have shown great success in automatic code generation. However, such large language model-based code generation techniques face the risk of introducing security vulnerabilities to codebases. In this work, we explore the direction of fine-tuning large language models for generating more secure code. We use real-world vulnerability fixes as our fine-tuning dataset. We craft a code-generation scenario dataset (C/C++) for evaluating and comparing the pre-trained and fine-tuned models. Our experiments on GPT-J show that the fine-tuned GPT-J achieved 70.4% and 64.5% ratios of non-vulnerable code generation for C and C++, respectively, which has a 10% increase for C and a slight increase for C++ compared with the pre-trained large language model.
Sun 14 AprDisplayed time zone: Lisbon change
16:00 - 17:30 | FORGE2024 Awards & Foundation Models for Code and Documentation GenerationResearch Track at Luis de Freitas Branco Chair(s): Antonio Mastropaolo Università della Svizzera italiana | ||
16:00 10mAwards | Award Ceremony Research Track | ||
16:10 7mShort-paper | Fine Tuning Large Language Model for Secure Code GenerationNew Idea Paper Research Track Junjie Li Concordia University, Aseem Sangalay Delhi Technological University, Cheng Cheng Concordia University, Yuan Tian Queen's University, Kingston, Ontario, Jinqiu Yang Concordia University | ||
16:17 14mFull-paper | Investigating the Performance of Language Models for Completing Code in Functional Programming Languages: a Haskell Case StudyFull Paper Research Track Tim van Dam Delft University of Technology, Frank van der Heijden Delft University of Technology, Philippe de Bekker Delft University of Technology, Berend Nieuwschepen Delft University of Technology, Marc Otten Delft University of Technology, Maliheh Izadi Delft University of Technology | ||
16:31 7mShort-paper | On Evaluating the Efficiency of Source Code Generated by LLMsNew Idea Paper Research Track Changan Niu Software Institute, Nanjing University, Ting Zhang Singapore Management University, Chuanyi Li Nanjing University, Bin Luo Nanjing University, Vincent Ng Human Language Technology Research Institute, University of Texas at Dallas, Richardson, TX 75083-0688 | ||
16:38 14mFull-paper | PathOCL: Path-Based Prompt Augmentation for OCL Generation with GPT-4Full Paper Research Track Seif Abukhalaf Polytechnique Montreal, Mohammad Hamdaqa Polytechnique Montréal, Foutse Khomh École Polytechnique de Montréal | ||
16:52 7mShort-paper | Creative and Correct: Requesting Diverse Code Solutions from AI Foundation ModelsNew Idea Paper Research Track Scott Blyth Monash University, Christoph Treude Singapore Management University, Markus Wagner Monash University, Australia | ||
16:59 7mShort-paper | Commit Message Generation via ChatGPT: How Far Are We?New Idea Paper Research Track | ||
17:06 24mOther | Discussion Research Track | ||