Higher Computing Science SQA

This subject is broken down into 24 topics in 4 modules:

  1. Software Design and Development 8 topics
  2. Computer Systems 4 topics
  3. Database Design and Development 5 topics
  4. Web Design and Development 7 topics
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This page was last modified on 28 September 2024.

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Computing Science

Software Design and Development

Development Methodologies

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Development Methodologies

Development Methodologies

Waterfall Methodology

  • The Waterfall Model is a sequential software development methodology that shifts through ['requirements', 'design', 'implementation', 'verification' and 'maintenance'] phases.
  • Each phase results in a deliverable that feeds into the next phase.
  • It assumes that requirements will remain stable after they are defined.
  • It focuses on extensive documentation, which makes it easier for new team members to get up to speed.
  • The drawback of the Waterfall methodology is that it does not reflect changes in requirements during the development process.

Agile Methodology

  • The Agile Methodology emphasises close collaboration between the programming team and business stakeholders; real-time communication, rather than writing extensive requirements upfront; and breaking the product into a series of incremental releases.
  • Agile methodologies allow for changing requirements over time by using time-boxed, iterative workflows known as sprints.
  • Agile place emphasis on face-to-face communication over written documents when the team is all in the same location.
  • It focuses on minimalistic method of doing just enough as necessary. (Just Enough Documentation).

Iterative and Incremental Development

  • Iterative and Incremental development methodology allows changes in project development stages. After each stage, the project is put to test and changes are made accordingly.
  • In Iterative and Incremental development methodology, development is segmented into small builds of the software where segments are divided on the basis of software functionality.
  • The iterative approach allows development to be more flexible, while the incremental part of this methodology allows development teams to demonstrate progress in more quantifiable terms.

Spiral Model

  • Spiral Model combines elements of both design and prototyping-in-stages, combining advantages of top-down and bottom-up concepts.
  • Each phase in spiral model starts with a design goal and ends with a review meeting.
  • Spiral Model involves risk analysis, and includes risk handling as a specific phase.
  • It is a good choice for large and complex projects.

Rapid Application Development (RAD)

  • RAD is suitable for projects where requirements are not clear and can be implemented in parallel.
  • RAD phase includes planning, user design, construction, cutover.
  • It is suitable when technical risks are less and high modularization is required.
  • RAD has shorter development time and higher customer satisfaction.

Course material for Computing Science, module Software Design and Development, topic Development Methodologies

Computing Science

Database Design and Development

Database Design and Development: Analysis

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Database Design and Development: Analysis

Database Design and Development: Analysis

Purpose of Database Analysis

  • Database analysis aims to discern and outline the specific requirements of a database system before it's developed. This step forms the basis for the design process.

  • A significant part of the analysis lies in understanding the organization's processes and how these would interact with the database.

  • The analysis helps in identifying the nature of data to be stored, retrieved, updated or deleted in the database.

User Requirements Analysis

  • In database analysis, thorough understanding of user requirements is crucial. Users of the database could range from employees within the organization to customers accessing the service.

  • Analyzing user interfaces, access needs and ongoing operations can provide useful insights about the functionality needed from the database.

  • Feedback from potential users during the analysis stage can avoid potential complications during implementation and usage.

Data Analysis

  • A comprehensive data analysis helps in determining the data types, their interrelationships and any constraints that should be imposed.

  • Data workflows and dependencies must be mapped out to ensure efficient and accurate database operations.

  • Develop an understanding of the frequency of data operations, for instance, how often data would be entered, updated or queried. This knowledge aids in designing database's structures and optimizing for performance.

Review of Existing Systems

  • Part of the analysis includes reviewing any existing systems and their shortcomings to improve upon them in the new database design and development.

  • Identify any legacy data, systems or processes that may need to be integrated with or migrated to the new database.

Documentation in Database Analysis

  • Documentation is a vital part of the analysis phase. All specifications gathered should be documented and organized systematically for reference and clarity.

  • This documentation serves as a blueprint for the subsequent design phase and can prevent confusion, errors and oversights.

  • Likewise, any changes to the database in the future would require updating this documentation. Thus, it's essential to keep it accurate and updated.

Remember, a thorough database analysis not only helps in creating a more relevant database design, but it can also save time, resources and potential issues in the future.

Course material for Computing Science, module Database Design and Development, topic Database Design and Development: Analysis

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