The Tool Desk
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What equivalence partitioning means
ISO/IEC/IEEE 29119-1:2022 defines an equivalence partition as a class of inputs or outputs expected to be treated similarly by the test item. Equivalence partitioning designs test cases to exercise those partitions using representative members. The ISTQB Certified Tester Foundation Level Syllabus v4.0.1 presents EP as a black-box technique for deriving test cases.
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A partition is a reasoned hypothesis about behavior, grounded in a specification, interface contract, or other test basis. A single test exercises a representative; it does not establish that every member behaves identically. ISTQB cautions that understanding how a test object treats different values can be complicated, so partitioning requires care.
How to build useful partitions
- Start with the test basis. Identify the requirement, contract, or expected behavior, then list the data and conditions that affect it.
- Group by expected treatment. Put values together only when the test object is expected to handle them similarly. Partitions may describe inputs, outputs, configuration items, internal values, time-related values, or interface parameters. They can be continuous or discrete, ordered or unordered, finite or infinite.
- Check the partition structure. Each partition must be non-empty, and the partitions should not overlap. Make sure the groups represent the relevant possibilities in the requirement rather than leaving unexplained gaps.
- Identify valid and invalid groups. A valid partition contains values that should be accepted or processed under the applicable interpretation; an invalid partition contains values expected to be rejected, ignored, or left without defined processing. State the interpretation: specifications and teams may define validity differently.
- Choose representatives and expected results. Select at least one value from each partition and record the expected result for that test. Include invalid partitions, not only accepted values.
- Review assumptions and risk. Revisit a group if the specification implies different outcomes within it, or if risk suggests that one representative is inadequate. Refine the partitions before treating coverage as meaningful.
Worked example: a specified numeric range
Suppose a requirement says a field accepts whole numbers from 18 through 65, inclusive, and rejects numbers outside that range with a validation error. That hypothetical requirement creates three partitions:
| Partition | Example representative | Expected result |
|---|---|---|
| Below range: values less than 18 (invalid) | 17 | Reject with a validation error |
| In range: values from 18 through 65, inclusive (valid) | 40 | Accept or process the value |
| Above range: values greater than 65 (invalid) | 66 | Reject with a validation error |
The values and expected results here follow the example requirement; they are not universal rules for numeric fields. If the actual contract accepts decimals, treats blank input specially, or uses a different error response, those behaviors need partitions or additional tests of their own.
What partition coverage tells you
EP coverage is the number of identified partitions exercised by at least one test case divided by the total number of identified partitions, expressed as a percentage:
EP coverage = (identified partitions exercised ÷ total identified partitions) × 100%
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Multiple parameters: each-choice coverage
For a feature with multiple parameters, track partitions for each parameter separately. Each-choice coverage means every partition in each parameter’s set appears in at least one test. It does not require every cross-parameter combination. For example, testing each color partition and each size partition independently may leave some color-size pairs untested. Where interactions matter, add combination-oriented tests rather than treating EP alone as combination coverage.
When to add boundary value analysis or decision tables
Choose the technique to match the requirement and the coverage question. EP targets representative behavior groups; boundary value analysis targets edges of ordered partitions; decision table testing targets combinations of conditions and the outcomes those combinations produce. These are complementary techniques, not a universal ranking.
Rank #4
Boundary value analysis
For the inclusive 18–65 example, boundary value analysis adds tests around 18 and 65, where mistakes in comparisons or limits are likely to matter. ISTQB describes two-value BVA as testing a boundary and its closest neighbor across the adjacent partition; three-value BVA tests the boundary and both neighbors. BVA focuses on partition boundaries—it does not replace identifying the partitions first.
Decision tables
Use a decision table when outcomes depend on combinations of conditions. EP can help identify behavior groups for individual conditions or values, but it does not by itself ensure that the combinations producing different outcomes have been exercised.
Best Value
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