Ticket Tagger: Machine Learning Driven Issue Classification
1. Does the paper propose a new opinion mining approach?
Yes
2. Which opinion mining techniques are used (list all of them, clearly stating their name/reference)?
ticket-tagger based on fast facebook fasttext classifier
3. Which opinion mining approaches in the paper are publicly available? Write down their name and links. If no approach is publicly available, leave it blank or None.
ticket-tagger: https://github.com/rafaelkallis/ticket-tagger fasttext: https://fasttext.cc/
4. What is the main goal of the whole study?
to automatically predict the labels to assign to issues (bug report, enhancement, and question)
5. What the researchers want to achieve by applying the technique(s) (e.g., calculate the sentiment polarity of app reviews)?
same as 4
6. Which dataset(s) the technique is applied on?
30,000 issues extracted from heterogeneous GitHub projects
7. Is/Are the dataset(s) publicly available online? If yes, please indicate their name and links.
https://tinyurl.com/y23kgdro
8. Is the application context (dataset or application domain) different from that for which the technique was originally designed?
retrained
9. Is the performance (precision, recall, run-time, etc.) of the technique verified? If yes, how did they verify it and what are the results?
10-fold cross validation, achieved F-measure values above 80% for each considered label
10. Does the paper replicate the results of previous work? If yes, leave a summary of the findings (confirm/partially confirms/contradicts).
No
11. What success metrics are used?
precision, recall and F-measure metrics
12. Write down any other comments/notes here.
tool demo