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  2. Data modeling - Wikipedia

    en.wikipedia.org/wiki/Data_modeling

    Data modeling techniques and methodologies are used to model data in a standard, consistent, predictable manner in order to manage it as a resource. The use of data modeling standards is strongly recommended for all projects requiring a standard means of defining and analyzing data within an organization, e.g., using data modeling:

  3. Goal-oriented Requirements Language - Wikipedia

    en.wikipedia.org/wiki/Goal-oriented_Requirements...

    Goal-oriented Requirements Language (GRL), an i*-based modeling language used in systems development, is designed to support goal-oriented modeling and reasoning about requirements especially the non-functional requirements [1]

  4. Requirements analysis - Wikipedia

    en.wikipedia.org/wiki/Requirements_analysis

    Recording requirements: Requirements may be documented in various forms, usually including a summary list, and may include natural-language documents, use cases, user stories, process specifications, and a variety of models including data models. Analyzing requirements: determining whether the stated requirements are clear, complete ...

  5. Goal modeling - Wikipedia

    en.wikipedia.org/wiki/Goal_modeling

    UML's use case diagram provides a simple goal modeling notation. The bubbles name functional goals, [14] so a Use case diagram forms a simple functions-only goal model: as Cockburn writes, use cases cover only the behavioral requirements. [15] Roles are shown as actors (stickmen on the diagram), linked to the use cases in which they take part.

  6. Requirements Modeling Framework - Wikipedia

    en.wikipedia.org/wiki/Requirements_Modeling...

    The Requirements Modeling Framework (RMF) is an open-source software framework for working with requirements based on the ReqIF standard. RMF consists of a core allowing reading, writing and manipulating ReqIF data, and a user interface allowing to inspect and edit request data.

  7. Database testing - Wikipedia

    en.wikipedia.org/wiki/Database_testing

    The techniques used in white box testing are condition coverage, decision coverage, statement coverage, cyclomatic complexity. The main advantage of white box testing in database testing is that coding errors are detected, so internal bugs in the database can be eliminated. The limitation of white box testing is that SQL statements are not covered.

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  9. Evolutionary database design - Wikipedia

    en.wikipedia.org/wiki/Evolutionary_database_design

    Evolutionary database design involves incremental improvements to the database schema so that it can be continuously updated with changes, reflecting the customer's requirements. People across the globe work on the same piece of software at the same time hence, there is a need for techniques that allow a smooth evolution of database as the ...