Goals
- get used to some of this ugly buzzword tools as they are used in a broad audience
- read out bugzilla bug description and try to find out, if the initial description (comment) has any deeper information of the bug
Bugzilla
The script 'py-bug.py' reads out the public bugs of bugzilla.opensuse.org, one by one, and writes the * first comment (bug description) * Summary * number of comments * creation time * time of last comment to a json file. Unforntunately I could not get the area/type of the bug, so something like 'kernel', 'yast'...
Tensorflow
The script 'json-reader.py' reads in the json file of the bugs and tries to learn if the inital bug description could be linked to the 'duration' (time of las comment - creation time) or the number of comments. For this the neuronal net could be modifed by commandline parameters
Lessons learned
- accelerated docker containers are not easy to install, had to use the pip package instead
- my GPU (1050Ti) is not so much faster than my CPU (Xeon E3-1231v3)
- could not train the modell to get any useful information, so no automatic bug resolution
Github Repo
https://github.com/mslacken/ml-bugs
Looking for hackers with the skills:
This project is part of:
Hack Week 18
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