2.2 Evaluating Computational Solutions

When a computational solution is developed, it must be evaluated against a set of criteria to ensure it is fit for purpose. The key evaluation metrics are efficiency, clarity, correctness, scalability, robustness, and the processes of verification and validation.

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1. Efficiency

Efficiency refers to how well a solution utilises available resources — primarily CPU time and memory (RAM) — to complete a task.

• Time Complexity measures how execution time grows with input size, expressed using Big-O notation (e.g., $O(n)$, $O(n^2)$, $O(\log n)$).
• Space Complexity measures how memory usage grows with input size.

An algorithm with $O(n)$ time complexity is generally more efficient than one with $O(n^2)$.

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2. Clarity

Clarity (also called readability or maintainability) refers to how easy it is for a programmer to read, understand, and modify the code.

A clear solution:

• Uses meaningful variable and function names
• Includes appropriate comments
• Follows consistent indentation and formatting
• Is logically structured with modular functions

Clarity is critical for long-term maintenance and team collaboration.

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3. Correctness

Correctness means the solution produces the right output for all valid inputs and handles edge cases appropriately.

A solution is correct if:

• It satisfies all specified requirements
• It handles boundary conditions (e.g., empty input, maximum values)
• It passes all test cases, including edge cases

Correctness is verified through testing and formal verification.

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4. Scalability

Scalability is the ability of a solution to handle an increasing workload (more users, more data) without a significant drop in performance.

• A web application designed for 100 users should ideally scale to handle 10,000 users.
• Scalable solutions often use efficient data structures and distributed architectures.

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5. Robustness

Robustness is the ability of a system to cope with errors during execution and handle erroneous or unexpected input gracefully, without crashing.

Examples of robust behaviour:

• Displaying an error message when a user enters a string where a number is expected
• Handling network timeouts without crashing the application
• Recovering from file-not-found errors

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6. Verification vs. Validation

These two processes are often confused but serve distinct purposes:

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Summary Table