Empirical Analysis of Affect of Merged Issues on GitHub
1. Does the paper propose a new opinion mining approach?
No
2. Which opinion mining techniques are used (list all of them, clearly stating their name/reference)?
a machine learning classifier: A. Murgia, M. Ortu, P. Tourani, B. Adams, and S. Demeyer. An exploratory qualitative and quantitative analysis of emotions in issue report comments of open source systems. Empirical Software Engineering, 23(1):521–564, 2018.
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.
None
4. What is the main goal of the whole study?
To study the relations with the affect expressed in pull-request issues’ comments and whether an issue is merged in the main branch or not
5. What the researchers want to achieve by applying the technique(s) (e.g., calculate the sentiment polarity of app reviews)?
To measure emotions
6. Which dataset(s) the technique is applied on?
a dataset of issues(in particular pull-request issues) from GHTorrent dataset identifying about ˜66K GitHub’s contributors (both users and developers), ˜153K issues and ˜1M comments from seven open source software
7. Is/Are the dataset(s) publicly available online? If yes, please indicate their name and links.
No
8. Is the application context (dataset or application domain) different from that for which the technique was originally designed?
No, almost same. GitHub issues vs Jira issues
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?
No
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?
N/A
12. Write down any other comments/notes here.
-