[Core] Simplify duplicate feature detection #1602
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Between #165 and #259 cucumber would start to ignore duplicate features by taking their MD5 sum and comparing newly parsed features. Replacing the set of MD5 sums with a map of source to features simplifies the code and allows duplicates to be logged as warnings. I couldn't discover any good reason to use an MD5 hash over javas `hashCode` and `equals`. 1) Memory consumption doesn't to be a problem. `CucumberFeature` already keeps a reference the original source. 2) Collision doesn't appear to be a problem. `hashCode` produces a 32 bit hash. So by the birth-day paradox math we'd need approximately 9000 feature files for a 1% chance of collision.
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Summary
Between #165 and #259 cucumber would start to ignore duplicate features
by taking their MD5 sum and comparing newly parsed features. Replacing
the set of MD5 sums with a map of source to features simplifies the code
and allows duplicates to be logged as warnings.
I couldn't discover any good reason to use an MD5 hash over javas
hashCodeandequals.Memory consumption doesn't seem to be a problem.
CucumberFeaturealready keeps a reference the original source.
Collision doesn't appear to be a problem.
hashCodeproduces a 32bit hash. So by the birth-day paradox math we'd need approximately 9000
feature files for a 1% chance of collision.
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