Results Tab¶
The Results tab displays the generated covering array and provides tools for analyzing and exporting the test cases.
Results Grid¶
After generation, the grid displays one row per test case with a column for each parameter.
Visual Indicators¶
| Indicator | Meaning |
|---|---|
| Green background | Seed row — a test case that was seeded (required) |
| Red text | Negative value — a value marked with ~ for negative testing |
| Row numbers | Sequential numbering in the leftmost column |
Coverage % Column¶
The Coverage % column shows the cumulative tuple coverage as you read down the rows. Each row's value represents the percentage of all required t-way tuples that have been covered by that row and all rows above it. The first row might show 15%, the second 28%, and so on, building toward 100% at the bottom.
This is useful for understanding how quickly coverage accumulates. In a well-optimized covering array, each row contributes significant new coverage early on, with diminishing returns toward the end.
Sorting¶
Click any column header to sort the grid. When you sort, the Coverage % column automatically recalculates based on the new row order. This lets you explore questions like "what if I ran these test cases in a different order?" — you can see how coverage would accumulate under that ordering.
Statistics Panel¶
Below the grid, a statistics panel shows:
| Statistic | Description |
|---|---|
| Total combinations | The exhaustive count (all possible combinations) |
| Test cases | How many test cases were generated |
| Negative test cases | Rows containing at least one negative (~) value |
| Seed rows matched | How many seed rows appear in the results |
Export Options¶
| Action | Shortcut | Description |
|---|---|---|
| Copy to Clipboard | Ctrl+Shift+C | Copies as tab-separated values (pastes into Excel) |
NCK Post-Optimization¶
Click Reduce using NCK to apply the NCK algorithm, which attempts to remove redundant test cases while maintaining full coverage. This can further reduce the test suite size.
NCK with SIPO
When using the SIPO engine, NCK post-optimization will rarely reduce the number of test cases because SIPO's simulated annealing algorithm is already so efficient at minimizing the covering array. NCK is most useful when applied to PICT-generated results.
See Concepts: NCK Optimization for details.