Computing case analysis: evaluate a proposed system
| English | 中文 | Pinyin |
|---|---|---|
| throughput/ˈθruːpʊt/ | 吞吐量 | tūn tǔ liàng |
| acceptance test/əkˈseptəns test/ | 验收测试 | yàn shōu cè shì |
What would explain this observation?
- A proposed school booking system looks efficient, but its recommendation depends on users, constraints and evidence. A plausible technical term alone is not a justified decision.
- Start with a prediction. State the quantities or features you would compare, then decide what evidence could distinguish two explanations.
Build the model
- A case study places computational choices within a stated scenario. Identify stakeholders, requirements, available evidence and constraints before recommending an architecture or algorithm. Distinguish a scenario fact from an assumption and a prediction. Trade-offs can involve performance, reliability, accessibility, privacy, maintenance and cost.
- throughput 吞吐量: Completed work per unit time under a stated workload; acceptance test 验收测试: A test of whether a specified user requirement is met.
Which statement best supports a technical recommendation?
A recommendation should connect a requirement to a mechanism and an observable test. For example, a concurrency problem requires a strategy that prevents conflicting updates, plus tests demonstrating the invariant. A claim about faster response requires comparable workload measurements rather than only a complexity label. Evaluate alternatives under the same stated conditions.
Match each technical term to its precise meaning.
Use the definitions to distinguish related quantities and processes.
Choose evidence that can test it
- A recommendation should connect a requirement to a mechanism and an observable test. For example, a concurrency problem requires a strategy that prevents conflicting updates, plus tests demonstrating the invariant. A claim about faster response requires comparable workload measurements rather than only a complexity label. Evaluate alternatives under the same stated conditions.
- Use an original school-approved scenario and synthetic data. Build a matrix of claim, supporting scenario evidence, technical explanation, limitation and acceptance test. Mark missing facts explicitly and show how the recommendation would change if an assumption failed. This prepares case-based reasoning without reproducing an unavailable IB assessment case study or inventing its examination rubric.
Which two habits make the investigation or model in this case more defensible?
Use an original school-approved scenario and synthetic data. Build a matrix of claim, supporting scenario evidence, technical explanation, limitation and acceptance test. Mark missing facts explicitly and show how the recommendation would change if an assumption failed. This prepares case-based reasoning without reproducing an unavailable IB assessment case study or inventing its examination rubric.
Work from known quantities
- State the known values and their units. Choose the relation because its assumptions fit this case, then rearrange before substitution.
- Known: a fictional prototype processes 120 successful requests during a 30-second observation. Throughput is 120/30=4 successful requests/s. A second prototype processes 150 in 30 seconds, or 5/s. The second has 25% greater recorded throughput, but no conclusion about latency, failure rate or performance under a larger workload follows without those measurements.
A fictional system successfully processes 210 requests in 30 s. Calculate throughput. Use the same sequence: known quantities → model → relation → substitution → unit and interpretation.
A fictional system successfully processes 210 requests in 30 s. Calculate throughput.
The result is 7 requests/s. Known: a fictional prototype processes 120 successful requests during a 30-second observation. Throughput is 120/30=4 successful requests/s. A second prototype processes 150 in 30 seconds, or 5/s. The second has 25% greater recorded throughput, but no conclusion about latency, failure rate or performance under a larger workload follows without those measurements.
Check the conclusion and its limits
- Throughput is not response time. A strong recommendation states the conditions in which it is expected to work and the evidence that could disconfirm it. Current official CS case-study requirements must be checked against the applicable assessment version.
- Return to the original observation. Explain what the result supports, which conditions it assumes, and one way to test a competing explanation.
Higher observed throughput automatically proves lower response time for every request. This claim is false: Throughput is not response time. A strong recommendation states the conditions in which it is expected to work and the evidence that could disconfirm it. Current official CS case-study requirements must be checked against the applicable assessment version.
Computing case analysis: evaluate a proposed system: A recommendation should connect a requirement to a mechanism and an observable test. For example, a concurrency problem requires a strategy that prevents conflicting updates, plus tests demonstrating the invariant. A claim about faster response requires comparable workload measurements rather than only a complexity label. Evaluate alternatives under the same stated conditions.
Higher observed throughput automatically proves lower response time for every request.
Throughput is not response time. A strong recommendation states the conditions in which it is expected to work and the evidence that could disconfirm it. Current official CS case-study requirements must be checked against the applicable assessment version.
Completed work per unit time under a stated workload: write the technical term.
throughput means Completed work per unit time under a stated workload.