Fuzzy string matching is a technique to search strings which are not a 100% match and match the specific pattern approximately, rather than exactly.
Let’s say I want to create a selector which needs to be resilient to user typos or similar user input with a key work I expect to be entered a search field.
First, I have opened up a bing.com in my borwser and searched for ‘uipath certification‘ text:
When I get a selector for the input field, the ‘title’ attribute will contain the searched text and the selector is valid:
However, if the user enters the text with typos or not the exact expected text, the selector becomes invalid and my automation will fail:
Lucky for me, the new fuzziness options are available and I can add them into the selector to make the selector smarter and more resistant to these changes:
By decreasing the fuzziness level (closer to 0), I can tune my selector to match lower similarities, while values closer to 1 will match higher similarities. In the example below, my fuzzy selector matches similar text (e.g ‘uipath certification exam’ vs the one included in the selector’s title attribute (e.g. ‘uipath certification’):
That’s it for this #FeatureBlog
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