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When it comes to production deployment, the two core practices are be rigorous and work with operations. By rigorous we mean: design build files that do not take shortcuts or make too many assumptions about systems such as where applications are installed. In addition, include many tests. Since chapter 12 in this book, we have been intermittently writing functional tests for the program and happiness tests for the system configuration. The happiness test, our taglib to probe for needed classes and other important configuration data, is one of our secret weapons for successful production deployment. If your functional tests fail, it could be the fault of your program or the system. If the configuration tests fail, it is the fault of the system. The more easily trouble can be located, the more easily it can be fixed. In addition, configuration problems don t merit waking the development team up at 3 a.m. in theory, anyway. In practice, it is going to happen. The purpose of the tests then becomes to give you 15 minutes more sleep before they call you. The other key purpose for tests is to verify that the program works on different application servers. We have explored some of the problems here, but there is no core way to address them other than to use the same application server everywhere. Sun is developing validation tools to help here, but there are so many other subtleties of deploying to and operating a different application server that it probably is not enough.

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Ant helps by providing the unified deployment and testing system. Even so, keeping the number of application servers to an absolute minimum one is very helpful. The other area of focus is working with operations. From its perspective, the ideal server is one that works so well, they forget where it is located or how to log in to it. It just works. It is far beyond the scope of this book to address the techniques needed to achieve such a goal. We have introduced our thoughts on a process that may move toward that goal: that of treating operations needs as use cases, and the problems it encounters as defects to be logged, tracked, tested, and fixed.

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I have observed many test teams add model-based testing to their arsenal of test techniques. I have seen teams succeed, and I have seen teams fail to adopt model-based testing successfully. Some of the most common errors I have observed when teams adopt model-based testing are the following: Too much modeling Teams that try to model everything end up testing nothing. The most important item to keep in mind when beginning to model is to start with small models of simple features. Remember that not everything is a candidate for modeling, and large models are difficult to maintain.

static void GetAQuoteCallback(IAsyncResult asyncResult) { ServiceReference1.QuotesServiceClient quotesServiceClient = (ServiceReference1.QuotesServiceClient) asyncResult.AsyncState; ServiceReference1.Quote quote = quotesServiceClient.EndGetAQuote(asyncResult); } Console.WriteLine(quote.StrQuote);

Running the target adds one more line to the compilation target s output; here stating that two files were deleted:

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9 . . Make the asynchronous call to GetAQuote . This is easy just call the QuotesServiceClient s BeginGetAQuote method from the Program class s Main method . Pass in the GetAQuoteCallback method you just wrote as the first parameter and the QuotesServiceClient object as the second parameter . Add a call to System.Console .ReadLine to pause the main thread so that the asynchronous call has time to execute .

compile: [depend] Deleted 2 out of date files in 0 seconds [javac] Compiling 3 source files to C:\AntBook\app\webapp\build\classes

Modeling doesn't replace other testing MBT is one tool of many that you will use to test. Teams that look to MBT as a silver bullet soon find that it isn't. Develop a test strategy that uses all of the tools in the test toolbox effectively. Only model what you can verify Random monkey tests can be interesting, but the bugs they find are extremely difficult to debug and diagnose. Good models include verification at every step that confirms that the current state conforms to the expected state. Design carefully When a tester makes a mistake or two writing a typical automated test, it just causes a test or two to report false results. When a tester makes a mistake creating a model, an entire suite of tests can break. The design of the model is critical and is worthy of extra caution and review especially as the team is learning modeling.

Because this task ensures that source code changes are picked up more reliably, we always use this task in our projects. Sometimes the fact that it cannot detect dependencies upon imported constants (static final data) catches us out, as their changes do not propagate: remember to clean build every time you change a public constant. A regular clean build is always a good idea. 10.2.4 Grammar parsing with JavaCC The Lucene indexing and search engine that we ve incorporated into our example application allows for sophisticated search expressions such as these:

using using using using using System; System.Collections.Generic; System.Linq; System.Text; System.Data;

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