exLong: Generating Exceptional Behavior Tests with Large Language Models
This program is tentative and subject to change.
Many popular programming languages, including C#, Java, and Python, support exceptions. Exceptions are thrown during program execution if an unwanted event happens, e.g., a method is invoked with an illegal argument value. Software developers write exceptional behavior tests (EBTs) to check that their code detects unwanted events and throws appropriate exceptions. Prior research studies have shown the importance of EBTs, but those studies also highlighted that developers put most of their efforts on “happy paths”, e.g., paths without unwanted events. To help developers fill the gap, we present the first framework, dubbed EXLÓNG, that automatically generates EBTs. EXLÓNG is a large language model instruction-tuned from CodeLlama and embeds reasoning about traces that lead to throw statements, conditional expressions that guard throw statements, and non-exceptional behavior tests that execute similar traces. We compare EX LÓNG with the state-of-the-art models for test generation (CAT-LM) and one of the strongest foundation models (GPT3.5), as well as with analysis-based tools for test generation (Randoop and EvoSuite). Our results show that EXLÓNG outperforms existing models and tools. Furthermore, we contributed several pull requests to open-source projects and 23 EBTs generated by EXLÓNG were already accepted.
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 | ||