John Holland Posted May 13 Share Posted May 13 Hi all, I have built an analysis on an asset tree and an SDL program that is becoming widely used and important for my colleagues. Usually, I would try to build such a tool using Test Driven Development. Years of experience of breaking things has taught me the benefits of this approach! However, I am struggling to build a strategy here and was wondering if there are any recommendations for doing this with the spy library. Thanks. Link to comment Share on other sites More sharing options...
Seeq Team Mark Derbecker Posted May 13 Seeq Team Share Posted May 13 There are a few things that you are likely trying to verify whenever you make changes: 1. The asset tree contains the signals, conditions and scalars you expect. 2. The results of the calculations within the tree are what you expect. For #1, I'd recommend creating a test (either in a notebook cell, or calling out to a separate .py file) that, after your tree has been pushed, utilizes `spy.search()` to "spot-check" important members of the tree that you expect to be there. You can use the `all_properties=True` flag if you'd like to check things like formulas. For #2, I'd recommend creating another test that uses `spy.pull()` to spot-check calculations at hard-coded time intervals and ensure that you're seeing (roughly) the values you expect by looking at the min / max / avg of the returned data. Although these are relatively "loose" tests, at least they will catch major regressions. You can further implement TDD methodology whenever you fix a "bug" in your tree going forward: First add a test that "fails" and illustrates the problem, then fix it such that the test passes. Then make that test part of the suite that you run after updating the tree. 1 Link to comment Share on other sites More sharing options...
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