What We Learned from Analyzing 100M Bugs
instabug.com
What We Learned from Analyzing 100M Bugs
1–10 of 31 posts
Re: What We Learned from Analyzing 100M Bugs
#2Hmm, all this is basically what I would expect....wait..."Most bugs are reported from iPhones, while more bugs/user are reported from LG devices."
....Why LG?
Re: What We Learned from Analyzing 100M Bugs
#3Hmm, all this is basically what I would expect....wait..."Most bugs are reported from iPhones, while more bugs/user are reported from LG devices." ....Why LG?
Thanks for checking out the report! I'm part of the team who put it together :)
Yeah, we thought LG was interesting too. When it comes to Android, we expected Samsung to take first place tbh, but we found more bugs/user reported from LG and Google devices (Fig. 9). This could be explained by our technical user base and the popularity of Nexus devices with Android developers. So the higher proportion of bugs/user we see reported is most likely due to internal beta testing by devs.
We went into this with some expectations and were surprised by some other findings as well... like Danish being the top locale where bugs/user are reported from :D
Re: What We Learned from Analyzing 100M Bugs
#4I think the y axis: "% Bugs" in Fig. 1 has an incorrect scale. It doesn't add to 100%.
Also, it doesn't seem consistent with the claim: "Bugs discovered through Instabug are most likely to be resolved within 24 hours of being reported"
Re: What We Learned from Analyzing 100M Bugs
#5Hmm, all this is basically what I would expect....wait..."Most bugs are reported from iPhones, while more bugs/user are reported from LG devices." ....Why LG?
Thanks for checking out the report! I'm part of the team who put it together :) Yeah, we thought LG was interesting too. When it comes to Android, we expected Samsung to take first place tbh, but we found more bugs/user reported from LG and Google devices (Fig. 9). This could be explained by our technical user base and the popularity of Nexus devices with Android developers. So the higher proportion of bugs/user we s…
>Most bugs are reported from iPhones
So this means kibd of that there are more iPhone users in Instabug? Like let's say population is 100. There are 70 iPhone, 10 Samsung, 10 LG, 10 Nokia. 70 iPhone users have 70 bugs, that is 1 bug per user, and similarly 1 bug per Samsung, 1 bug per Nokia. But 15 bugs for 10 LGs. That is 1,5 bug per LG user. In short; 70 bugs in iPhone is actually same or may be better than Samsung. It really makes no sense expet that there are more iPhone users. The only useful information is that LG has more bugs per user. In other words the most useful information is persantage. I'm sure you don't have any Statistician in your work envirement because if you had he would say "let's remove that 'Most bugs are reported from iPhones' part because it makes no sense'. Guys don't hire computer science graduates as statisticians (buzz word: data scientist), hire statistics graduates as statisticians (buzz word: data scientist).
Re: What We Learned from Analyzing 100M Bugs
#6I think the y axis: "% Bugs" in Fig. 1 has an incorrect scale. It doesn't add to 100%. Also, it doesn't seem consistent with the claim: "Bugs discovered through Instabug are most likely to be resolved within 24 hours of being reported"
This is what happens when you hire computer science graduates as data scientists, you have incorrect data that you believe is correct. On the other hand those who hire statisticians are more successfull in collecting and analizing data.
Re: What We Learned from Analyzing 100M Bugs
#7I think the y axis: "% Bugs" in Fig. 1 has an incorrect scale. It doesn't add to 100%. Also, it doesn't seem consistent with the claim: "Bugs discovered through Instabug are most likely to be resolved within 24 hours of being reported"
This is what happens when you hire computer science graduates as data scientists, you have incorrect data that you believe is correct. On the other hand those who hire statisticians are more successfull in collecting and analizing data.
As one of my better bosses used to say:
Graphs are for asking better questions, not for making decisions.
Such a bad plan in fact that Mark Twain has a joke about it.
Re: What We Learned from Analyzing 100M Bugs
#8I think the y axis: "% Bugs" in Fig. 1 has an incorrect scale. It doesn't add to 100%. Also, it doesn't seem consistent with the claim: "Bugs discovered through Instabug are most likely to be resolved within 24 hours of being reported"
The figure is showing percent of all bugs, not percent of resolved bugs. Likely the rest of the 100% is unresolved bugs.
The confusion on the second point hinges in "most likely". You're likely interpreting that as the expectation of resolution time whereas they are using maximum likelyhood estimation. MLE is rather useless in this case, but it is technically still correct.
Re: What We Learned from Analyzing 100M Bugs
#9Pretty much all of these "most" findings are explainable by the distribution of the installed user base, i.e. they are not real results but just artifacts of the population sizes.
Re: What We Learned from Analyzing 100M Bugs
#10I think the y axis: "% Bugs" in Fig. 1 has an incorrect scale. It doesn't add to 100%. Also, it doesn't seem consistent with the claim: "Bugs discovered through Instabug are most likely to be resolved within 24 hours of being reported"
The figure is showing percent of all bugs, not percent of resolved bugs. Likely the rest of the 100% is unresolved bugs. The confusion on the second point hinges in "most likely". You're likely interpreting that as the expectation of resolution time whereas they are using maximum likelyhood estimation. MLE is rather useless in this case, but it is technically still correct.
Yep, that clears up my confusion. Thanks