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Object Oriented Analysis And Design With UML

by Jntu Heroes
Type: NoteInstitute: JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY Downloads: 334Views: 5190Uploaded: 8 months agoAdd to Favourite

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Object Oriented Analysis And Design With UML by Jntu Heroes

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Jntu Heroes
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UNIT I Introduction to UML Syllabus : Importance of modeling, principles of modeling, object oriented modeling, conceptual model of the UML, Architecture, and Software Development Life Cycle. Object -oriented modeling languages appeared sometime between the mid 1970s and the late 1980s as methodologists, faced with a new genre of object-oriented programming languages and increasingly complex applications, began to experiment with alternative approaches to analysis and design. The number of object-oriented methods increased from fewer than 10 to more than 50 during the period between 1989 and 1994. Many Importance of modeling, principles of modeling, object oriented modeling, conceptual model of the UML, Architecture, and Software Development Life Cycle. Brief History of the UML users of these methods had trouble finding a modeling language that met their needs completely, thus fueling the so- called method wars. Learning from experience, new generations of these methods began to appear, with a few clearly prominent methods emerging, most notably Booch, Jacobson's OOSE (ObjectOriented Software Engineering), and Rumbaugh's OMT (Object Modeling Technique). Other important methods included Fusion, Shlaer-Mellor, and Coad-Yourdon. Each of these was a complete method, although each was recognized as having strengths and weaknesses. In simple terms, the Booch method was particularly expressive during the design and construction phases of projects, OOSE provided excellent support for use cases as a way to drive requirements capture, analysis, andhigh-level design, and OMT-2 was most useful for analysis and data-intensive information systems. The behavioral component of many object-oriented methods, including the Booch method and OMT, was the language of statecharts, invented by David Harel. Prior to this object-oriented adoption, statecharts were used mainly in the realm of functional decomposition and structured analysis, and led to the development of executable models and tools that generated full running code. A critical mass of ideas started to form by the mid 1990s, when Grady Booch (Rational Software Corporation), Ivar Jacobson (Objectory), and James Rumbaugh (General Electric) began to adopt ideas from each other's methods, which collectively were becoming recognized as the leading object-oriented methods worldwide. As the primary authors of the Booch, OOSE, and OMT methods, we were motivated to create a unified modeling language for three reasons. First, our methods were already evolving toward each other independently. It made sense to continue that evolution together rather than apart, eliminating the potential for any unnecessary and gratuitous differences that would further confuse users. Second, by unifying our methods, we could bring some stability to the object-oriented marketplace, allowing projects to settle on one mature modeling language and letting tool builders focus on delivering more useful features. Third, we expected that our collaboration would yield improvements for all three earlier methods, helping us to capture lessons learned and to address problems that none of our methods previously handled well. As we began our unification, we established three goals for our work: 1. To model systems, from concept to executable artifact, using object- oriented techniques 2. To address the issues of scale inherent in complex, mission-critical systems 3. To create a modeling language usable by both humans and machines Devising a language for use in object- oriented analysis and design is not unlike designing a programming language. First, we had to constrain the problem: Should the language encompass requirements specification? Should the language be sufficient to permit visual programming? Second, we had to strike a 1
balance between expressiveness and simplicity. Too simple a language would limit the breadth of problems that could be solved; too complex a language would overwhelm the mortal developer. In the case of unifying existing methods, we also had to be sensitive to the installed base. Make too many changes, and we would confuse existing users; resist advancing the language, and we would miss the opportunity of engaging a much broader set of users and of making the language simpler. The UML definition strives to make the best trade-offs in each of these areas. The UML effort started officially in October 1994, when Rumbaugh joined Booch at Rational. Our project's initial focus was the unification of the Booch and OMT methods. The version 0.8 draft of the Unified Method (as it was then called) was released in October 1995. Around the same time, Jacobson joined Rational and the scope of the UML project was expanded to incorporate OOSE. Our efforts resulted in the release of the UML version 0.9 documents in June 1996. Throughout 1996, we invited and received feedback from the general software engineering community. During this time, it also became clear that many software organizations saw the UML as strategic to their business. We established a UML consortium, with several organizations willing to dedicate resources to work toward a strong and complete UML definition. Those partners contributing to the UML 1.0 definition included Digital Equipment Corporation, Hewlett-Packard, I-Logix, Intellicorp, IBM, ICON Computing, MCI Systemhouse, Microsoft, Oracle, Rational, Texas Instruments, and Unisys. This collaboration resulted in the UML 1.0, a modeling language that was well-defined, expressive, powerful, and applicable to a wide spectrum of problem domains. UML 1.0 was offered for standardization to the Object Management Group (OMG) in January 1997, in response to their request for proposal for a standard modeling language. Between January 1997 and July 1997, the original group of partners was expanded to include virtually all of the other submitters and contributors of the original OMG response, including Andersen Consulting, Ericsson, ObjecTime Limited, Platinum Technology, PTech, Reich Technologies, Softeam, Sterling Software, and Taskon. A semantics task force was formed, led by CrisKobryn of MCI Systemhouse and administered by Ed Eykholt of Rational, to formalize the UML specification and to integrate the UML with other standardization efforts. A revised version of the UML (version 1.1) was offered to the OMG for standardization in July 1997. In September 1997, this version was accepted by the OMG Analysis and Design Task Force (ADTF) and the OMG Architecture Board and then put up for vote by the entire OMG membership. UML 1.1 was adopted by the OMG on November 14, 1997. A successful software organization is one that consistently deploys quality software that meets the needs of its users. An organization that can develop such software in a timely and predictable fashion, with an efficient and effective use of resources, both human and material, is one that has a sustainable business. There's an important implication in this message: The primary product of a development team is not beautiful documents, world-class meetings, great slogans, or Pulitzer prize— winning lines of source code. Rather, it is good software that satisfies the evolving needs of its users and the business. Everything else is secondary. Unfortunately, many software organizations confuse "secondary" with "irrelevant." To deploy software that satisfies its intended purpose, you have to meet and engage users in a disciplined fashion, to expose the real requirements of your system. To develop software of lasting quality, you have to craft a solid architectural foundation that's resilient to change. To develop software rapidly, efficiently, and effectively, with a minimum of software scrap and rework, you have to have the right people, the right tools, and the right focus. To do all this consistently and predictably, with an appreciation for the lifetime costs of the system, you must have a sound development process that can adapt to the changing needs of your business and technology. Modeling is a central part of all the activities that lead up to the deployment of good software. We build models to communicate the desired structure and behavior of our system. We build models to visualize and control the system's architecture. We build models to better understand the system we are building, often exposing opportunities for simplification and reuse. We build models to manage risk. 2
The Importance of Modeling Unsuccessful software projects fail in their own unique ways, but all successful projects are alike in many ways. There are many elements that contribute to a successful software organization; one common thread is the use of modeling. Modeling is a proven and well-accepted engineering technique. We build architectural models of houses and high rises to help their users visualize the final product. We may even build mathematical models in order to analyze the effects of winds or earthquakes on our buildings. Modeling is not just a part of the building industry. It would be inconceivable to deploy a new aircraft or an automobile without first building models• from computer models to physical wind tunnel models to full-scale prototypes. New electrical devices, from microprocessors to telephone switching systems require some degree of modeling in order to better understand the system and to communicate those ideas to others. In the motion picture industry, storyboarding, which is a form of modeling, is central to any production. In the fields of sociology, economics, and business management, we build models so that we can validate our theories or try out new ones with minimal risk and cost. What, then, is a model? Simply put, A model is a simplification of reality. A model provides the blueprints of a system. Models may encompass detailed plans, as well as more general plans that give a 30,000-foot view of the system under consideration. Why do we model? There is one fundamental reason. We build models so that we can better understand the system we are developing. Through modeling, we achieve four aims. 1. Models help us to visualize a system as it is or as we want it to be. 2. Models permit us to specify the structure or behavior of a system. 3. Models give us a template that guides us in constructing a system. 4. Models document the decisions we have made. Modeling is not just for big systems. Even the software equivalent of a dog house can benefit from some modeling. However, it's definitely true that the larger and more complex the system, the more important modeling becomes, for one very simple reason: We build models of complex systems because we cannot comprehend such a system in its entirety. Principles of Modeling The use of modeling has a rich history in all the engineering disciplines. That experience suggests four basic principles of modeling. First, The choice of what models to create has a profound influence on how a problem is attacked and how a solution is shaped. In other words, choose your models well. The right models will brilliantly illuminate the most wicked development problems, offering insight that you simply could not gain otherwise; the wrong models will mislead you, causing you to focus on irrelevant issues. 3
Second, Every model may be expressed at different levels of precision. If you are building a high rise, sometimes you need a 30,000- foot view• for instance, to help your investors visualize its look and feel. Other times, you need to get down to the level of the studs• for instance, when there's a tricky pipe run or an unusual structural element. Third, The best models are connected to reality. A physical model of a building that doesn't respond in the same way as do real materials has only limited value; a mathematical model of an aircraft that assumes only ideal conditions and perfect manufacturing can mask some potentially fatal characteristics of the real aircraft. It's best to have models that have a clear connection to reality, and where that connection is weak, to know exactly how those models are divorced from the real world. All models simplify reality; the trick is to be sure that your simplifications don't mask any important details. In software, the Achilles heel of structured analysis techniques is the fact that there is a basic disconnect between its analysis model and the system's design model. Failing to bridge this chasm causes the system as conceived and the system as built to diverge over time. In object-oriented systems, it is possible to connect all the nearly independent views of a system into one semantic whole. Fourth, No single model is sufficient. Every nontrivial system is best approached through a small set of nearly independent models. If you are constructing a building, there is no single set of blueprints that reveal all its details. At the very least, you'll need floor plans, elevations, electrical plans, heating plans, and plumbing plans. Object-Oriented Modeling In software, there are several ways to approach a model. The two most common ways are from an algorithmic perspective and from an object-oriented perspective. The traditional view of software development takes an algorithmic perspective. In this approach, the main building block of all software is the procedure or function. This view leads developers to focus on issues of control and the decomposition of larger algorithms into smaller ones. There's nothing inherently evil about such a point of view except that it tends to yield brittle systems. As requirements change (and they will) and the system grows (and it will), systems built with an algorithmic focus turn out to be very hard to maintain. The contemporary view of software development takes an object-oriented perspective. In this approach, the main building block of all software systems is the object or class. Simply put, an object is a thing, generally drawn from the vocabulary of the problem space or the solution space; a class is a description of a set of common objects. Every object has identity (you can name it or otherwise distinguish it from other objects), state (there's generally some data associated with it), and behavior (you can do things to the object, and it can do things to other objects, as well). The Unified Modeling Language (UML) is a standard language for writing software blueprints. The UML may be used to visualize, specify, construct, and document the artifacts of a software-intensive system. 4

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