Curriculum Portal

neurologyYou need to know

• A problem is suitable for a computational solution when it can be expressed through defined inputs, processes and outputs that algorithms can perform.
• Computationally solvable problems usually contain tasks that can be decomposed, represented as data and completed through precise, repeatable rules.
• Stakeholders are individuals or groups who affect, use or are affected by the solution, and their needs may include functionality, accessibility, usability, security and performance.
• Research into the problem domain, existing solutions and relevant algorithms helps identify effective approaches and avoid unsuitable design choices.
• The essential features of a proposed solution are the functions required to meet stakeholder needs and solve the identified problem.
• A solution's limitations are constraints or weaknesses such as restricted scope, platform dependence, limited data, performance boundaries or features that cannot be implemented.
• Solution requirements define what the product must do, while hardware and software requirements identify the environment needed to develop and operate it.
• Success criteria must be specific, measurable and linked directly to the problem, stakeholder needs and required functionality.
• Decomposition divides a complex problem into smaller modules that can be designed, implemented and tested separately before integration.
• A complete design explains the overall structure of the solution and uses algorithms to define the processing performed by each component.
• Usability features include clear navigation, consistent layouts, meaningful feedback, error prevention, accessible presentation and appropriate help for users.
• Variables store individual values, data structures organise related values, and classes combine attributes and methods to represent entities in an object-oriented solution.
• Validation checks whether input data is sensible and acceptable, whereas verification checks whether data has been copied or entered accurately.
• Normal test data represents valid, typical input; boundary data tests values at or immediately around permitted limits; erroneous data is invalid and should be rejected or handled safely.
• Iterative development involves repeatedly designing, implementing, testing and refining parts of the solution using evidence from each iteration.
• A prototype is a partial or simplified implementation used to investigate algorithms, interfaces or technical feasibility before completing the final solution.
• Annotated development evidence links code, interfaces, test results and changes to the decisions made during each development stage.
• Remedial action is a justified change made after testing reveals a defect, weakness or unmet requirement, and retesting confirms whether the change worked.
• Evaluation uses development tests, robustness tests and stakeholder usability feedback to judge the completed solution against its original measurable success criteria.
• Maintainability depends on features such as modular structure, meaningful identifiers, clear documentation, low coupling and code that can be corrected or extended without unnecessary changes.

• Justify the suitability of a problem for computational methods by identifying its inputs, outputs, data, processes, constraints and opportunities for automation.
• Identify relevant stakeholders and explain how specific functional and usability requirements address their needs.
• Research the problem domain and comparable solutions, then use cited findings to justify algorithms, features and technical approaches while recognising limitations.
• Specify functional, hardware and software requirements and produce measurable success criteria that can be verified through testing.
• Decompose the problem into coherent modules and represent the complete solution using suitable algorithms, interfaces, variables, data structures, classes and validation rules.
• Design a test plan containing normal, boundary and erroneous data, expected outcomes and reasons for each test across iterative and post-development testing.
• Develop the solution iteratively, annotating prototypes, code and interface evidence to explain decisions and demonstrate progress towards the design.
• Test each development stage, compare actual results with expected results, diagnose faults, apply justified remedial changes and retest affected components.
• Conduct final robustness and usability testing, recording objective results and stakeholder feedback as annotated evidence.
• Evaluate the final product against every success criterion using test evidence, then assess usability, maintainability, limitations and realistic opportunities for further development.