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hahaha2019yz avatar hahaha2019yz commented on May 16, 2024

@jaccz Hi, Jack, do you have any idea of this problem? Shall we exchange our options?

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jaccz avatar jaccz commented on May 16, 2024

The o:1 feature has a bug somewhere only recently discovered (#40). I haven't had time to root cause it yet. I predict your issue is a duplicate of that. One thing to check: if you use /o:2 instead of /o:1, will you see the same issue?

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hahaha2019yz avatar hahaha2019yz commented on May 16, 2024

The o:1 feature has a bug somewhere only recently discovered (#40). I haven't had time to root cause it yet. I predict your issue is a duplicate of that. One thing to check: if you use /o:2 instead of /o:1, will you see the same issue?

Hi, jaccz @jaccz , thanks for reply.
I've saw the problem of #40, maybe it is not the same with mine. My problem is because the second combination is random, the PICT program gave the second submodel a set of random value, but this set of value was cutted off by the stricts(this maybe wouldn't happen by giving another random set of value).
So if we could process this problem by taking all the parameters under a total root model(without generate submodels), and generate the test cases by using strength @1 of all the paramters, such as below:

E D C 0 0 0 0 0 0 0 0
E D B 0 0 0 0
E D A 0 0 0 0
E C B 0 0 0 0
E C A 0 0 0 0
E B A 0 0
D C B 0 0 0 0
D C A 0 0 0 0
D B A 0 0
C B A 0 0
I H 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I G 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I F 0 0 0 0 0 0 0 0 0 0
H G 255 255 0 0 255 255 0 0 255 255 0 0 0 0 255 255
H F 0 0 0 0 0 0 0 0
G F 0 0 0 0 0 0 0 0

As all of above values must at least appear once in the test cases, we suppose the problem I've mentioned above may be resolved.

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hahaha2019yz avatar hahaha2019yz commented on May 16, 2024

Hi, jaccz @jaccz ,
I also have another question about the PICT:
1. What's the benifits of separate the problem of many submodels? The efficiency of algorithm, or to reduce the complex of the problem?

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lakshmishub avatar lakshmishub commented on May 16, 2024

Sub-models will help you to keep the number of cases low. you can only select the set of input variables that will directly impact the output , instead of all inout variables generating the combinations

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