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How do I design test cases systematically?
- Read the requirement and identify observable behavior, constraints, rules, and any relevant states or events.
- Choose a model that fits the problem: classes of inputs or outputs, ordered limits, combinations of conditions, or state changes.
- Write down the partitions, boundaries, decision rules, or transitions before choosing specific test data.
- For each test, record its preconditions, input or event, expected result, and the requirement or model element it checks.
- Review the design for omitted invalid inputs, missing combinations, unreachable states, and adjacent boundary values where they matter.
- Add structural or experience-based testing when source-code structure or practitioner knowledge reveals risks that the specification-derived model does not cover.
A test design technique derives tests from a basis or model; it is not a complete test strategy by itself. Selection depends on the system, risk, requirements, applicable standards, and practitioner skill. The overview of Foundation Level techniques distinguishes black-box, white-box, experience-based, and collaboration-based approaches, but the four black-box methods below are the ones described in depth here: ASTQB’s overview of ISTQB Foundation Level test design techniques.
Choose the technique that matches the behavior
| Technique | Use when | What to derive | Design review focus |
|---|---|---|---|
| Equivalence partitioning | Values fall into groups expected to be processed alike. | Representative cases from relevant valid and invalid partitions. | Whether the classes and their expected treatment are justified. |
| Boundary value analysis | Partitions are ordered and behavior may differ at their limits. | Boundary values and adjacent values, using the selected two-value or three-value variant. | Which edges are included, whether limits are inclusive, and what increment is meaningful. |
| Decision table testing | Different combinations of conditions determine different outcomes. | Relevant condition combinations and the action for each rule. | Which combinations matter and whether rules can be simplified without losing required coverage. |
| State-transition testing | Current state and events affect what the system does next. | State changes and paths through the model. | Required state, transition, or path coverage in light of risk. |
These are not competing universal methods. Compare their fit with the test basis, likely defects, expected behavior, and the coverage goal. A single feature can call for more than one model.
Equivalence partitioning: test representative classes
Equivalence partitioning (EP) divides input, output, internal, time, or interface values into groups expected to receive the same treatment. Choose representative values from each relevant valid and invalid class rather than testing every value indiscriminately. This is a test-design heuristic, not proof that every untested value in a class is defect-free.
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Example: an age field
If a requirement accepts whole-number ages from 18 through 120 inclusive, the initial partitions are below 18 (invalid), 18–120 (valid), and above 120 (invalid). EP suggests selecting at least one representative from each relevant class. Then use boundary analysis to focus on the edges, since representative-only testing may not reveal an off-by-one error.
Boundary value analysis: probe ordered limits
Boundary value analysis (BVA) targets the edges of ordered partitions, where adjacent values may be handled differently. The Foundation Level material describes two-value and three-value variants. For the age example, the meaningful increment is one year because the requirement concerns whole-number ages; do not reuse these cases for a domain with continuous values, a different increment, or different inclusivity rules.
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Two-value variant
Test each boundary and the adjacent value outside it: 17, 18, 120, and 121. This covers the lower and upper limits and their immediate outside neighbors.
Three-value variant
Test below, on, and above each boundary: 17, 18, 19, 119, 120, and 121. This adds the immediate inside neighbor at each limit. Choose the variant that matches the coverage need and risk; state which one you used so reviewers can understand the intended edge coverage.
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Decision tables: make rule combinations explicit
When several conditions combine to determine an outcome, a decision table makes the combinations and resulting actions visible. ASTQB’s page presenting ISTQB Foundation Level syllabus material states: “Decision tables are used for testing the implementation of requirements that specify how different combinations of conditions result in different outcomes.” See the ASTQB explanation.
Start by listing the conditions and possible outcomes, then create a rule column for each relevant combination and write the expected action for each column. Derive tests from those rules. During review, check that required combinations are represented and that any simplification preserves the coverage your risk and requirements call for. Decision tables are particularly useful when a sentence containing several “and,” “or,” or exception clauses is difficult to translate into individual cases.
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State-transition testing: include history and event order
Use state-transition testing when the same event can produce different results depending on the system’s current state. Define the meaningful states, events, and any guards on transitions, then derive cases that exercise the state changes and paths that matter. A useful test record includes the starting state, event or guarded event, expected next state, and observable result.
Choose state, transition, or path coverage according to the behavior and risk being tested; no single coverage target is right for every system. Review the model for missing transitions and unreachable states, and consider sequences where event order changes the outcome. This technique addresses history-dependent behavior that a set of independent input partitions may not represent.
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Combine models when a feature has more than one risk shape
For an age-based eligibility workflow, EP can separate acceptable and unacceptable ages, BVA can probe the inclusive age limits, a decision table can express how age interacts with other eligibility conditions, and state-transition testing can cover what happens after an application is submitted or rejected. These models answer different questions; applying one does not make the others redundant.
The wider toolkit also includes white-box, experience-based, and collaboration-based techniques. Add them when code structure, practitioner knowledge, or collaboration exposes risks not captured by the specification-derived models. The overview source recognizes those families, but the material cited here does not support a detailed treatment of their specific methods.
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