厚客户端的性能测试 [英] Performance testing of Thick client
问题描述
我有一个厚客户端应用程序要测试。我们需要下载jnlp文件，然后启动它，提供所需的凭据，然后单击提交。然后启动应用程序。
I have a thick client application to be tested. We need to downlod the jnlp file and then launch it, supply required credentials and click submit. It then launches the application.
我是性能测试的新手，想要对这个应用程序进行性能测试。
I'm newbie to performance testing and want to performance test this application. Which open source tool will be useful, which is the best tool for testing thick clients?
推荐答案
什么是开放源代码工具对于测试胖客户端是最好的工具？厚客户测试？如果它是关于它启动，下载或操作多快，你需要循环到桌面自动化软件。目前可用于桌面测试自动化的最佳开源工具是：
What do you mean by "Performance testing of Thick client"? If it is about how fast it launches, downloads or operates than you need to loop into desktop automation software. The best open source tools currently available for desktop testing automation are:
如果您的应用程序执行网络通信，并且您的方案假设模拟数百或数千个同时使用以下开源工具可以帮助：
If your application does network communication and your scenario assumes simulating hundreds or thousands of simultaneous thick clients communicating with the server using the following open-source tools can help:
- Grinder
- Gatling
- JMeter
- Tsung
如果您的应用程式支援Proxy .jnlp - 它是基于Java的应用程序，它肯定会），协议是HTTP（S），您将能够记录测试场景和重放。 Grinder 工具记录代理可以降低级别并执行TCP协议记录。
If your application supports proxy (given .jnlp - it is Java-based application and it surely does) and the protocol is HTTP(S) you will be able to record test scenario and replay. The Grinder tool recording proxy can go a lower level and perform TCP protocol recording.
有关上面列出的性能测试工具的主要功能比较的更多信息，请查看开源负载测试工具：你应该使用哪一个？
For more information on main features comparison for performance testing tools listed above look into Open Source Load Testing Tools: Which One Should You Use? post.
如果您的应用程序使用一种安全通信或专有协议，您应该能够通过将应用程序库插入JMeter，Grinder或Gatling来复制它的行为，并使用它们的多线程和报告功能，以执行大量负载测试。
If your application uses a form of secure communication or proprietary protocol you should be able to replicate it's behavior by plugging your application libraries to JMeter, Grinder or Gatling and use their multithreading and reporting features to perform massive load testing.
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