Missing Negative Cases When Using Submodels
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Description
I found some weird situations in which all negative values of one submodel are ignored without any warning or error. This is the smallest model I found where this happens:
C1: 1, ~0
C2: 1, 2, 3, ~0
D1: ok, ~fail
D2: 1, ~0, ~-1
D3: 1, ~0, ~-1, ~-2, ~-3
{C1, C2} @ 2
{D1, D2, D3} @ 2
Current Behavior:
pict model /r:1222
C1 C2 D1 D2 D3
1 1 ok 1 1
1 3 ok 1 1
1 2 ok 1 1
1 1 ok ~-1 1
1 2 ok 1 ~-3
1 1 ~fail 1 1
1 3 ok 1 ~-1
1 3 ok ~0 1
1 2 ok ~-1 1
1 2 ok 1 ~0
1 2 ok ~0 1
1 3 ok 1 ~0
1 3 ok ~-1 1
1 1 ok ~0 1
1 1 ok 1 ~-3
1 2 ok 1 ~-1
1 3 ok 1 ~-3
1 1 ok 1 ~0
1 3 ~fail 1 1
1 1 ok 1 ~-1
1 1 ok 1 ~-2
1 2 ok 1 ~-2
1 3 ok 1 ~-2
1 2 ~fail 1 1
Expected Behavior:
- Expected test cases with
C1=~0 - Expected test cases with
C2=~0
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- Read the whole issue, then the project's contributing guide.
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- Open a pull request that references the issue number.
Research direction
Reproduce the issue with the model in the report using pict with /r:1222, then trace how submodels handle negative values. Done means generated cases include C1=~0 and C2=~0, with regression coverage for this model.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- cpp
- Domain
- testing
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 45/100