This program is tentative and subject to change.
Automated library APIs testing is difficult as it requires exploring a vast space of parameter inputs that may involve objects with complex data types. Existing search based approaches, with limited knowledge of relations between object states and program branches, often suffer from the low efficiency issue, i.e., tending to generate invalid inputs. Symbolic execution based approaches can effectively identify such relations, but fail to scale to large programs.
In this work, we present an LLM-based input space partitioning testing approach, LISP, for library APIs. The approach leverages LLMs to understand the code of a library API under test and perform input space partitioning based on its understanding and rich common knowledge. Specifically, we provide the signature and code of the API under test to LLMs, with the expectation of obtaining a text description of each input space partition of the API under test. Then, the generated text description is employed to guide the input generation process for each partition, ultimately resulting in test suites that systematically explore the program behavior of the API.
We evaluate LISP on 10 popular open-source Java libraries (e.g., apache/commons-lang with 2.6k stars, guava with 48.8k stars on GitHub). Our experiment results show that LISP is effective in library API testing. It significantly outperforms state-of-the-art tool EvoSuite in terms of branch coverage. On average, LISP achieves 67.82% branch coverage, surpassing EvoSuite by 1.21 times. In total, LISP triggers 404 exceptions or errors in the experiments, and discovers 13 previously unknown vulnerabilities during evaluation, which have been assigned CVE IDs.
This program is tentative and subject to change.
Thu 1 MayDisplayed time zone: Eastern Time (US & Canada) change
11:00 - 12:30 | |||
11:00 15mTalk | A Multi-Agent Approach for REST API Testing with Semantic Graphs and LLM-Driven Inputs Research Track Myeongsoo Kim Georgia Institute of Technology, Tyler Stennett Georgia Institute of Technology, Saurabh Sinha IBM Research, Alessandro Orso Georgia Institute of Technology | ||
11:15 15mTalk | ClozeMaster: Fuzzing Rust Compiler by Harnessing LLMs for Infilling Masked Real Programs Research Track Hongyan Gao State Key Laboratory for Novel Software Technology, Nanjing University, Yibiao Yang Nanjing University, Maolin Sun Nanjing University, Jiangchang Wu State Key Laboratory for Novel Software Technology, Nanjing University, Yuming Zhou Nanjing University, Baowen Xu State Key Laboratory for Novel Software Technology, Nanjing University | ||
11:30 15mTalk | LLM Based Input Space Partitioning Testing for Library APIs Research Track Jiageng Li Fudan University, Zhen Dong Fudan University, Chong Wang Nanyang Technological University, Haozhen You Fudan University, Cen Zhang Georgia Institute of Technology, Yang Liu Nanyang Technological University, Xin Peng Fudan University | ||
11:45 15mTalk | Leveraging Large Language Models for Enhancing the Understandability of Generated Unit Tests Research Track Amirhossein Deljouyi Delft University of Technology, Roham Koohestani Delft University of Technology, Maliheh Izadi Delft University of Technology, Andy Zaidman Delft University of Technology | ||
12:00 15mTalk | exLong: Generating Exceptional Behavior Tests with Large Language Models Research Track Jiyang Zhang University of Texas at Austin, Yu Liu Meta, Pengyu Nie University of Waterloo, Junyi Jessy Li University of Texas at Austin, USA, Milos Gligoric The University of Texas at Austin | ||
12:15 15mTalk | TOGLL: Correct and Strong Test Oracle Generation with LLMs Research Track | ||