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The same way we have text classification for the spam in our mail inbox, couldn't someone train a model to classify issues as actionable bugs vs. noise for larg
by woko 5y ago
The same way we have text classification for the spam in our mail inbox, couldn't someone train a model to classify issues as actionable bugs vs. noise for large projects like the one mentioned in the OP? Data would come from closed issues:
- if the issue lead to a commit, or a merge is mentioned in the thread, then it is actionable,
- if the issue was closed without any code change, then it is noise.
- convolvatron 5y agomaybe. but i think this process really helps develop a deeper understanding of the evolution of the project and doesn't take that much time. and I bet your AI isn't going to be able to say 'oh yeah, thats just that thing we fixed in 2.1' seems useful in a second-order capacity though - process introspection
- 542458 5y agoYou do see a decent number of GitHub bots that will trash issues if they don’t comply to some sort of “expected/observed/steps/specs” format, which I think more or less accomplishes the same thing. The only issue is you’re potentially losing out on issues from less-tech-savvy users, but I guess simply using GitHub filters many of those people anyways.