ICSE 2024
Fri 12 - Sun 21 April 2024 Lisbon, Portugal

Writing software tests is laborious and time-consuming. To address this, prior studies introduced various automated test-generation techniques. A well-explored research direction in this field is unit test generation, wherein artificial intelligence (AI) techniques create tests for a method/class under test. While many of these techniques have primarily found applications in a research context, existing tools (\textit{e.g.} \textit{EvoSuite}, Randoop, and AthenaTest) are not user-friendly and are tailored to a single technique. This paper introduces \textit{TestSpark}, a plugin for IntelliJ IDEA that enables users to generate unit tests with only a few clicks directly within their Integrated Development Environment (IDE). Furthermore, \textit{TestSpark} also allows users to easily modify and run each generated test and integrate them into the project workflow. \textit{TestSpark} leverages the advances of search-based test generation tools, and it introduces a novel technique to generate unit tests using Large Language Models (LLMs) by creating a feedback cycle between the IDE and the LLM. Since \textit{TestSpark} is an open-source (https://github.com/JetBrains-Research/TestSpark), extendable, and well-documented tool, it is possible to add new test generation methods into the plugin with the minimum effort.

\textbf{Demo video:} https://youtu.be/0F4PrxWfiXo

Thu 18 Apr

Displayed time zone: Lisbon change

11:00 - 12:30
Language Models and Generated Code 2Demonstrations / Research Track at Maria Helena Vieira da Silva
Chair(s): Reyhaneh Jabbarvand University of Illinois at Urbana-Champaign
11:00
15m
Talk
Exploring the Potential of ChatGPT in Automated Code Refinement: An Empirical Study
Research Track
Qi Guo Tianjin University, China, Junming Cao Fudan University, Xiaofei Xie Singapore Management University, Shangqing Liu Nanyang Technological University, Xiaohong Li Tianjin University, Bihuan Chen Fudan University, Xin Peng Fudan University
11:15
15m
Talk
Deep Learning or Classical Machine Learning? An Empirical Study on Log-Based Anomaly Detection
Research Track
BoXi Yu The Chinese University of Hong Kong, Shenzhen, Jiayi Yao The Chinese University of Hong Kong, Shenzhen, Qiuai Fu Huawei Cloud Computing Technologies CO., LTD., Zhiqing Zhong Chinese University of Hong Kong, Shenzhen, Haotian Xie The Chinese University of Hong Kong, Shenzhen, Yaoliang Wu Huawei Cloud Computing Technologies Co., Ltd., Yuchi Ma Huawei Cloud Computing Technologies CO., LTD., Pinjia He Chinese University of Hong Kong, Shenzhen
11:30
15m
Talk
TRACED: Execution-aware Pre-training for Source Code
Research Track
Yangruibo Ding Columbia University, Benjamin Steenhoek Iowa State University, Kexin Pei The University of Chicago, Gail Kaiser Columbia University, Wei Le Iowa State University, Baishakhi Ray Columbia University
11:45
15m
Talk
On Extracting Specialized Code Abilities from Large Language Models: A Feasibility Study
Research Track
Li Zongjie Hong Kong University of Science and Technology, Chaozheng Wang The Chinese University of Hong Kong, Pingchuan Ma HKUST, Chaowei Liu National University of Singapore, Shuai Wang Hong Kong University of Science and Technology, Daoyuan Wu Nanyang Technological University, Cuiyun Gao Harbin Institute of Technology, Yang Liu Nanyang Technological University
12:00
15m
Talk
When Neural Code Completion Models Size up the Situation: Attaining Cheaper and Faster Completion through Dynamic Model Inference
Research Track
Zhensu Sun Singapore Management University, Xiaoning Du Monash University, Australia, Fu Song State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, and University of Chinese Academy of Sciences Beijing, China, Shangwen Wang National University of Defense Technology, Li Li Beihang University
Pre-print
12:15
7m
Talk
TestSpark: IntelliJ IDEA’s Ultimate Test Generation Companion
Demonstrations
Arkadii Sapozhnikov JetBrains Research, Mitchell Olsthoorn Delft University of Technology, Annibale Panichella Delft University of Technology, Vladimir Kovalenko JetBrains Research, Pouria Derakhshanfar JetBrains Research
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