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> Also, if you look at the video from Youtube, you can see that 60% + 30% = 100% They said it catches 60% of bugs and has 30% false alarms. Those are different
by chrisfosterelli 9y ago
> Also, if you look at the video from Youtube, you can see that 60% + 30% = 100%
They said it catches 60% of bugs and has 30% false alarms. Those are different measurements, the sum of true positives and false positives doesn't need to add up to 100%.
- lrem 9y agoSo you're saying that 10% of time it says there's no bug? ;)
- deleted 9y ago[deleted]
- eftychis 9y ago> So you're saying that 10% of time it says there's no bug? ;) No. Recall the wording: "They said it catches 60% of bugs and has 30% false alarms. Those are different measurements, the sum of true positives and false positives doesn't need to add up to 100%." Hence (nitpicking perhaps): 100%-60%=40% of the bugs (their estimation) are not caught (false negatives) The rate of false positives is 30%, that is if a particular code excerpt has no bugs it might be tagged as buggy. Thus the final error rate I guess is (actual probability of a bug)60% + (1- (actual probability of a bug))30%.
- qbrass 9y agoWhat it does mean is that that unless 1/3 or more of your code is buggy, it will report more false positives than bugs.
- chrisfosterelli 9y agoYeah considering in terms of a binary classification the accuracy is not super impressive imo. It could just be learning statistical patterns for code that is more likely to have issues rather than identifying actual bugs. They did mention is does other things such as suggesting improvements, so maybe it does provide more tangible value in those ways. Hard to say without more information!
- iMart1n_FR 9y agoThank you for the clarification. Anyway, 60% of the time, it works every time !