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Rational Unified Process The Rational Unified Process (RUP, from Rational Software Corporation) is an iterative, prescriptive, architecture-centric process in which the code is merely one of the artifacts delivered by iterations of the project; design models and code documentation are usually other key artifacts. Use cases the intended uses of the system direct the design and development of the system. These provide insight into the problem domain, and a focus for development. The RUP divides the software process into four phases (inception, elaboration, construction, and transition), whereas different tasks (business modeling, analyzing and designing requirements, implementing, testing, and deploying) consume different resources in the different phases. There can be a number of iterations in each phase, and even in later phases, the high-level tasks in the process, such as analyzing and designing requirements, still take place. This enables yet controls change. Rational provides a large and integrated suite of products to automate the different stages of the software process. Their modeling tool, Rational Rose, and configuration management tool, ClearCase, are probably the most well known. Where we have personally found the RUP weak is that it delays deployment until the end of development. For large web or enterprise applications, deployment is one of the difficult and risky parts of the process. So, it should be emphasized sooner and fed into the basic architecture (Loughran 2002a), as well as into every iteration. This does not mean that RUP is unnecessary for such projects, merely insufficient. Scott Ambler, a leading proponent of the Agile Software movement, has proposed that developers explicitly include production as a new phase for the RUP, and operations and infrastructure management as new tasks in a project (Ambler 2001). This revised model aligns better with our experience, and enables Ant to act as the core of a continuous deployment system. Ant integrates with the Unified Process as a means of automating the compilation and deployment stages. It also supports the Rational ClearCase revision control tool with tasks to check files in and out. Ant does not integrate with Rational s testing product family, such as Visual Test or Purify; the latter code analysis tool would be a valuable task to be able to run from a build. The Rational TestSuite is more of a higher level test system than the JUnit tests, and would be hard to integrate unless the Rational product family actually hosted Ant itself.
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This book covers how to use Ant, starting with first principles and finishing with large projects complete with complex deployment, testing challenges, and extensions of Ant s capabilities. We also develop a Java application as we go along, because without a project there is no need for a build process. We base most of our examples on this project; although for some of the side topics, such as native code integration, we include stand-alone examples.
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Documentation search engine example Ant project The example project is a text search engine for indexing and searching through HTML documentation files such as the Ant documentation itself. This will work from the command line, and on an application server. To avoid having to write most of the search engine ourselves, we chose to use the Lucene search engine, which is another subproject of the Apache Jakarta group. It is Java-based, fast, and flexible. We also will integrate with Struts to provide a model/view/controller framework, and a data tier that can easily switch between a simple object-relational (O/R) framework and EJB to provide data persistence. For the complete source code of this application, visit the companion Manning web site. Our example project consists of several components designed for generalized reuse and illustrates how Ant not only automates mundane tasks, but adds tremendous value by accomplishing tasks that are simply not possible without it. Here are details of our project with chapter cross-references: We wrote a custom Ant task to create a Lucene index at build time. We are currently indexing Ant s own documentation, but it can index any set of HTML or text files. 19 covers creating custom Ant tasks and the Ant API. Incorporating custom Ant tasks into a build file becomes much easier if a taskname/classname-mapping properties file is included with the custom task distributable. We accomplish the automatic generation of this mapping file using XDoclet templates and custom tags. This eliminates the need for a possibly missed manual step, retains metadata in a single place, and reduces duplication. We introduce XDoclet in chapter 11. We developed stand-alone command-line tools to allow indexing and searching. Executing Java programs from within Ant is one topic of chapter 5. We wrote a web application incorporating a build-time generated index. The web application uses Struts, and later Apache Axis for its Web Service entry point. We start building a WAR file in chapter 6, and deploy it in chapter 7. Incorporating third-party (or internally versioned) libraries takes the spotlight in chapter 10. 12 goes into detail on customizing and testing web applications, and chapter 15 adds SOAP support to the build. We developed a common component to be the Lucene liaison used by the command-line applications and web application. This allows a level of abstraction so that our applications do not directly know that Lucene is the search engine. Our search engine could theoretically be replaced without affecting any code in any of the applications. This common component is in its own Ant build, the control of which we cover in chapter 9. Each of our individual builds reuses common pieces such as library mapping properties, directory naming conventions, and general build settings. XML entity references are detailed in appendix B.
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