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|a eng
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| 100 |
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|9 534987
|a Nassar, Heba A.
|e Author
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| 245 |
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|a Generation of Sequence Diagram Automatically from Use Case Model Using Genetic Algorithm
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| 260 |
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|a عمان
|c 2017
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| 300 |
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|a 1 - 67
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| 336 |
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|a رسائل جامعية
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| 502 |
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|b رسالة ماجستير
|c جامعة الاسراء الخاصة
|f كلية تكنولوجيا المعلومات
|g الاردن
|o 0009
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| 520 |
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|a Sequence diagram is a method used for presenting the details of interactions between users and system’s components. Sequence diagram helps in transition to a more formal level of refinement of the requirement description. Typically, system analysts are responsible for performing this process, and they usually perform the developing of sequence diagram manually. The aim of this thesis is to develop a software tool that generates sequence diagram (s) from flow of events founded in use case model, which is called Intelligent Sequence Diagram Generator (ISDG). This tool belongs to Intelligent Computer Aided Software Engineering (I-CASE) tools that have some sort of intelligence to perform those tasks that need human intellectual skills. Thematic role principle is used to distinguish the components of sequence diagram from the statement of flow of events. Semantic Role Labelling software type of Natural Language Processing (NLP) tools is used for automatically discovering the thematic role of each word in the input statement. The proposed solution delivered by this research passes through defining a new algorithmic approach for developing sequence diagram with two versions; manual and semi-automated. The final step is to convert the semi-automated version to a fully one by using of Genetic Algorithm (GA) approach for selecting the classification rules of extracting elements of sequence diagram from the natural language form statements of flow of events. This tool had been implemented using C# programming language of visual studio that support embedding other software components, and has graphical facilities for drawing the sequence diagram and developing GUI for the tool. The evaluation of the results has been handled using confusion matrix, in which the accuracy of the ISDG reach > 77%.
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| 653 |
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|a الخوارزمية الجينية
|a نموذج الحالة
|a الذكاء الاصطناعي
|a اللغة الطبيعية المعالجة
|a مخطط التسلسل
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| 700 |
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|9 534556
|a Al-Hroob, Aysh
|e Advisor
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| 700 |
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|9 534568
|a الزبيدي، إياد
|g Al-Zobaydi, Ayad T.
|e مشرف
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| 856 |
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|u 9802-021-007-0009-T.pdf
|y صفحة العنوان
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| 856 |
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|u 9802-021-007-0009-A.pdf
|y المستخلص
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| 856 |
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|u 9802-021-007-0009-C.pdf
|y قائمة المحتويات
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| 856 |
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|u 9802-021-007-0009-F.pdf
|y 24 صفحة الأولى
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| 856 |
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|u 9802-021-007-0009-1.pdf
|y 1 الفصل
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| 856 |
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|u 9802-021-007-0009-2.pdf
|y 2 الفصل
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| 856 |
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|u 9802-021-007-0009-3.pdf
|y 3 الفصل
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| 856 |
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|u 9802-021-007-0009-4.pdf
|y 4 الفصل
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| 856 |
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|u 9802-021-007-0009-O.pdf
|y الخاتمة
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| 856 |
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|u 9802-021-007-0009-R.pdf
|y المصادر والمراجع
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| 856 |
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|u 9802-021-007-0009-S.pdf
|y الملاحق
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| 930 |
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|d y
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| 995 |
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|a Dissertations
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| 999 |
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|c 991858
|d 991858
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