Replies: 6 comments 2 replies
|
|
|
The suppression feature (suppress output variables of tasks/fields of record type) is particularly important to link data correctly between compose and run steps. If not done properly especially if a compose step is doing a persistent read from various data stores (where data is being produced by another run step or another compose step), leads to extra data in output of compose steps. As a consequence unwanted chaining of compose and run steps will occur leading to ambiguous multiple "consumes-from-step", breaking the transformation chain. So it is necessary to take into account "suppress" variables to do the linking properly in the generated abstract tspec. |
|
|
When there is an error in the PSpec file, Python files are still generated—even though they may be invalid. For example, if the BPMN model name doesn't match the expected Rule_ID format, the Python files and the tspec_abstract folder are still created despite the error. |
|
Chaining of references: LHS and RHS matching Validation. (Elaborate. This is important. ) |
Uh oh!
There was an error while loading. Please reload this page.
Uh oh!
There was an error while loading. Please reload this page.
I will enumerate here model validations ideas I come up with while modeling:
All reactions