💥 Add docker image to generate tests in isolated sandbox environment.
💥 Added multithreading feature for faster test generation.
💥 Plugin now exports runtime and error logs.
💥 Custom prompt support added.
💥 Algorithm optimized to minimize token usage.
💥 Expanded configuration options. Refer to Steps to Run for details.
Many people have tried using ChatGPT to help them with various programming tasks and have achieved good results. However, there are some issues with using ChatGPT directly. Firstly, the generated code often fails to execute correctly, leading to the famous saying "five minutes to code, two hours to debug". Secondly, it is inconvenient to integrate with existing projects as it requires manual interaction with ChatGPT and switching between different platforms. To address these problems, we have proposed the "Generate-Validate-Repair" framework and implemented a prototype. Additionally, to make it easier for everyone to use, we have developed some plugins that can be seamlessly integrated into existing development workflows.
See chenyi26/chatunitest.
You can configure the plugin with the following parameters to your pom.xml file:
<plugin>
<groupId>io.github.ZJU-ACES-ISE</groupId>
<artifactId>chatunitest-maven-plugin</artifactId>
<version>1.4.1</version>
<configuration>
<!-- Required: You must specify your OpenAI API keys. -->
<apiKeys></apiKeys>
<model>gpt-3.5-turbo</model>
</configuration>
</plugin>Here's a detailed explanation of each configuration option:
apiKeys: (Required) Your OpenAI API keys. Example:Key1, Key2, ....model: (Optional) The OpenAI model. Default:gpt-3.5-turbo.url: (Optional) The API to use your model. Default:"https://api.openai.com/v1/chat/completions".testNumber: (Optional) The number of tests for each method. Default:5.maxRounds: (Optional) The maximum rounds of the repair process. Default:5.minErrorTokens: (Optional) The minimum tokens of error message in the repair process. Default:500.temperature: (Optional) The OpenAI API parameters. Default:0.5.topP: (Optional) The OpenAI API parameters. Default:1.frequencyPenalty: (Optional) The OpenAI API parameters. Default:0.presencePenalty: (Optional) The OpenAI API parameters. Default:0.proxy: (Optional) Your host name and port number if you need. Example:127.0.0.1:7078.selectClass: (Optional) Class under test. If the project contains the same class name in different packages, the className should be specified as the fully qualified name.selectMethod: (Optional) Method under test.tmpOutput: (Optional) The output path for parsed information. Default:/tmp/chatunitest-info.testOutput: (Optional) The output path for tests generated bychatunitest. Default:{basedir}/chatunitest.project: (Optional) The target project path. Default:{basedir}.thread: (Optional) Enable multi-threaded execution. Default:true.maxThread: (Optional) The maximum number of threads. Default:CPU cores * 5.stopWhenSuccess: (Optional) Stop the repair process when the test passes. Default:true.noExecution: (Optional) Skip the execution verification step of generated tests. Default:false.merge: (Optional) Merge all tests for each focal class. Default:true.promptPath: (Optional) Path to your custom prompt. Refer to default prompt insrc/main/resources/prompt.obfuscate: (Optional) Enable prompt obfuscation for enhanced privacy protection. Default:false.obfuscateGroupIds: (Opional) Group IDs you want to obfuscate. Default: only your group ID.
All these parameters also can be specified in the command line with -D option.
Essentially, the only thing you need to provide are your API keys.
If you use local LLM (such as code-llama), simply specify the model and url in the configuration:
<plugin>
<groupId>io.github.ZJU-ACES-ISE</groupId>
<artifactId>chatunitest-maven-plugin</artifactId>
<version>1.4.1</version>
<configuration>
<!-- Required: Use any string to replace your API keys -->
<apiKeys>xxx</apiKeys>
<model>code-llama</model>
<url>http://0.0.0.0:8000/v1/chat/completions</url>
</configuration>
</plugin><dependency>
<groupId>io.github.ZJU-ACES-ISE</groupId>
<artifactId>chatunitest-starter</artifactId>
<version>1.4.0</version>
<type>pom</type>
</dependency>First, you need to install your project and download all necessary dependencies, which can be done by running mvn install command.
You can run the plugin with the following command:
Generate unit tests for the target method:
mvn chatunitest:method -DselectMethod=className#methodNameGenerate unit tests for the target class:
mvn chatunitest:class -DselectClass=classNameYou must specify selectMethod and selectClass when executing mvn chatunitest:method or mvn chatunitest:class.
This is done using the -D option.
Example:
public class Example {
public void method1(Type1 p1, ...) {...}
public void method2() {...}
...
}
To test the class Example and all methods in it:
mvn chatunitest:class -DselectClass=ExampleTo test the method method1 in the class Example (Now ChatUnitest will generate tests for all methods named method1
in the class)
mvn chatunitest:method -DselectMethod=Example#method1Generate unit tests for the whole project:
mvn chatunitest:projectClean the generated tests:
mvn chatunitest:cleanRunning this command will delete all generated tests and restore your test folder.
Run the generated tests manually:
mvn chatunitest:copyRunning this command will copy the generated tests into the src/test/java/ directory for your convenience when you want
to run the tests manually. Your test folder will be backed up in the src/back/ directory.
If the merge configuration is enabled, you can run the test suites instead of individual tests for each class.
mvn chatunitest:restoreRunning this command will restore the test folder from the backup in the src/back/ directory.
mvn chatunitest:generateCoverage
<plugin>
<groupId>io.github.ZJU-ACES-ISE</groupId>
<artifactId>chatunitest-maven-plugin</artifactId>
<version>1.3.0</version>
<configuration>
<targetDir>D:\\coverage</targetDir>
<mavenHome>C:\\software\\apache-maven-3.9.2</mavenHome>
<sourceDir>chatunitest</sourceDir>
</configuration>
</plugin>Running this method will execute the tests in the folder sourceDir, the coverage
result of the project will remove to folder targetDir.
mvn chatunitest:generateMethodCoverage_separate<plugin>
<groupId>io.github.ZJU-ACES-ISE</groupId>
<artifactId>chatunitest-maven-plugin</artifactId>
<version>1.3.0</version>
<configuration>
<targetDir>D:\\coverage</targetDir>
<mavenHome>C:\\software\\apache-maven-3.9.2</mavenHome>
<sourceDir>chatunitest</sourceDir>
</configuration>
</plugin>Running this method will execute all the test classes in sourceDir separately
and calculate separate coverage for each test class, the result are saved in the methodCoverage_SEPARATE.json file under targetDir.
mvn chatunitest:generateMethodCoverage_mergeRunning this method will group the test classes according to the target test class(
the test classes for the same method will be summarized into a group). For test classes in the same grouping, the
program generates test coverage by subdividing the groups from 1-1 to 1-n
and the test coverage calculation will be performed by group, the result are saved in the
methodCoverage_MERGE.json file under targetDir.
This Maven plugin can be executed in various operating systems with different Java Development Kits (JDK) and Maven versions. The following environments have been tested and proven to work:
- Environment 1: Windows 11 / Oracle JDK 11 / Maven 3.9
- Environment 2: Windows 10 / Oracle JDK 11 / Maven 3.6
- Environment 3: Ubuntu 22.04 / OpenJDK 11 / Maven 3.6
- Environment 4: Darwin Kernel 22.1.0 / Oracle JDK 11 / Maven 3.8
Please note that these environments are tested and known to work. You can also try running the plugin in similar environments. If you encounter any issues in other environments, please refer to the documentation or seek appropriate support.
- Add code obfuscation to avoid sending the original code to ChatGPT.
- Add expense estimation and quota.
- Optimize the structure of generated test cases.
Our work has been submitted to arXiv. Check it out here: ChatUniTest.
@misc{xie2023chatunitest,
title={ChatUniTest: a ChatGPT-based automated unit test generation tool},
author={Zhuokui Xie and Yinghao Chen and Chen Zhi and Shuiguang Deng and Jianwei Yin},
year={2023},
eprint={2305.04764},
archivePrefix={arXiv},
primaryClass={cs.SE}
}
If you have any questions or would like to inquire about our experimental results, please feel free to contact us via email. The email addresses of the authors are as follows:
- Corresponding author:
zjuzhichen AT zju.edu.cn - Author:
yh_ch AT zju.edu.cn,xiezhuokui AT zju.edu.cn
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