By Aslak Knutsen, Andrew Lee Rubinger
Examine a use-case strategy for constructing Java firm functions in a consistently test-driven style. With this hands-on advisor, authors and Jboss venture leaders Andrew Lee Rubinger and Aslak Knutsen help you construct high-level parts, from chronic garage to the person interface, utilizing the Arquillian checking out platform and a number of other Jboss initiatives and tools.
Through the process the e-book, you’ll construct a production-ready software program convention tracker known as GeekSeek, utilizing resource code from GitHub. Rubinger and Knutsen show why checking out is the very beginning of development—essential for making sure that code is consumable, whole, and correct.
* Bootstrap an user-friendly Java Ee venture from begin to end prior to diving into the full-example software, GeekSeek
* Use either relational and NoSql garage versions to construct and try GeekSeek’s info endurance layers
* take on testable enterprise good judgment improvement and asynchronous messaging with an Smtp service
* reveal firm providers as a Restful interface, utilizing Java Ee’s Jax-Rs framework
* enforce Oauth authentication with Jboss’s Picket
* hyperlink id administration service
* Validate the Ui via automating interplay within the browser and interpreting the rendered page
* practice full-scale integration trying out at the ultimate deployable archive
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Extra resources for Continuous Enterprise Development in Java
Testing an entire application is the only way to know how code will actually run. 3. Isolating performance at a modular or operational level—a mes‐ obenchmark—offers a reasonable compromise but is no substi‐ tute for testing the full application. Common Code Examples Many of the examples throughout the book are based on a sample application that calculates the “historical” high and low price of a stock over a range of dates, as well as the standard deviation during that time. Historical is in quotes here because in the application, all the data is fictional; the prices and the stock symbols are randomly generated.
A performance test is only repeatable if the envi‐ ronment is the same from run to run; the automation must take care of that. Measure everything The automation must gather every conceivable piece of data that will be useful for later analysis. This includes system information sampled throughout the run: CPU usage, disk usage, network usage, memory usage, and so on. It includes logs from the application—both those the application produces, and the logs from the garbage collector. Ideally it can include Java Flight Recorder (JFR) recordings (see Chap‐ ter 3) or other low-impact profiling information, periodic thread stacks, and other heap analysis data like histograms or full heap dumps (though the full heap dumps, in particular, take a lot of space and cannot necessarily be kept long term).
The typical development cycle does not make things any easier. A project schedule often establishes a feature-freeze date: all feature changes to code must be checked into the repository at some early point in the release cycle, and the remainder of the cycle is devoted to shaking out any bugs (including performance issues) in the new release. This causes two problems for early testing: 1. Developers are under time constraints to get code checked in to meet the schedule; they will balk at having to spend time fixing a performance issue when the schedule has time for that after all the initial code is checked in.