This program is tentative and subject to change.
Misconfigurations are major causes of software failures. Existing practices rely on developer-written rules or test cases to validate configurations, which are expensive. Machine learning (ML) for configuration validation is considered a promising direction, but has been facing challenges such as the need of large-scale field data and system-specific models. Recent advances in Large Language Models (LLMs) show promise in addressing some of the long-lasting limitations of ML-based configuration validation. We present a first analysis on the feasibility and effectiveness of using LLMs for configuration validation. We empirically evaluate LLMs as configuration validators by developing a generic LLM-based configuration validation framework, named Ciri. Ciri employs effective prompt engineering with few-shot learning based on both valid configuration and misconfiguration data. Ciri checks outputs from LLMs when producing results, addressing hallucination and nondeterminism of LLMs. We evaluate Ciri’s validation effectiveness on eight popular LLMs using configuration data of ten widely deployed open-source systems. Our analysis (1) confirms the potential of using LLMs for configuration validation, (2) explores design space of LLMbased validators like Ciri, and (3) reveals open challenges such as ineffectiveness in detecting certain types of misconfigurations and biases towards popular configuration parameters.
This program is tentative and subject to change.
Thu 1 MayDisplayed time zone: Eastern Time (US & Canada) change
14:00 - 15:30 | |||
14:00 15mTalk | Large Language Models as Configuration Validators Research Track Xinyu Lian University of Illinois at Urbana-Champaign, Yinfang Chen University of Illinois at Urbana-Champaign, Runxiang Cheng University of Illinois at Urbana-Champaign, Jie Huang University of Illinois at Urbana-Champaign, Parth Thakkar Meta Platforms, Inc., Minjia Zhang UIUC, Tianyin Xu University of Illinois at Urbana-Champaign | ||
14:15 15mTalk | LLM Assistance for Memory Safety Research Track Nausheen Mohammed Microsoft Research, Akash Lal Microsoft Research, Aseem Rastogi Microsoft Research, Subhajit Roy IIT Kanpur, Rahul Sharma Microsoft Research | ||
14:30 15mTalk | Vulnerability Detection with Code Language Models: How Far Are We? Research Track Yangruibo Ding Columbia University, Yanjun Fu University of Maryland, Omniyyah Ibrahim King Abdulaziz City for Science and Technology, Chawin Sitawarin University of California, Berkeley, Xinyun Chen , Basel Alomair King Abdulaziz City for Science and Technology, David Wagner UC Berkeley, Baishakhi Ray Columbia University, New York;, Yizheng Chen University of Maryland | ||
14:45 15mTalk | Combining Fine-Tuning and LLM-based Agents for Intuitive Smart Contract Auditing with Justifications Research Track Wei Ma , Daoyuan Wu The Hong Kong University of Science and Technology, Yuqiang Sun Nanyang Technological University, Tianwen Wang National University of Singapore, Shangqing Liu Nanyang Technological University, Jian Zhang Nanyang Technological University, Yue Xue , Yang Liu Nanyang Technological University | ||
15:00 15mTalk | Towards Neural Synthesis for SMT-assisted Proof-Oriented ProgrammingAward Winner Research Track Saikat Chakraborty Microsoft Research, Gabriel Ebner Microsoft Research, Siddharth Bhat University of Cambridge, Sarah Fakhoury Microsoft Research, Sakina Fatima University of Ottawa, Shuvendu K. Lahiri Microsoft Research, Nikhil Swamy Microsoft Research | ||
15:15 15mTalk | Prompt-to-SQL Injections in LLM-Integrated Web Applications: Risks and Defenses Research Track Rodrigo Resendes Pedro INESC-ID / IST, Universidade de Lisboa, Miguel E. Coimbra INESC-ID / IST, Universidade de Lisboa, Daniel Castro INESC-ID / IST, Universidade de Lisboa, Paulo Carreira INESC-ID / IST, Universidade de Lisboa, Nuno Santos INESC-ID / Instituto Superior Tecnico, University of Lisbon |