Developer Behavior and Sentiment from Data Mining Open Source Repositories

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)?

Lexicon -based technique proposed by the authors. The workflow is implemented in KNIME, the lexicon is the NRC lexicon 0.92

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.

NRC lexicon https://saifmohammad.com/WebPages/NRC-Emotion-Lexicon.htm

4. What is the main goal of the whole study?

Understand the association between the routine diversity/routine change and change in the sentiment expressed by developers

5. What the researchers want to achieve by applying the technique(s) (e.g., calculate the sentiment polarity of app reviews)?

Calculate the emotion of the GitHub issues.

6. Which dataset(s) the technique is applied on?

Git events from 124 OSS projects The authors start with 148 projects and then select approximately 35 projects from each of the following sets: 1. >= 10,000 stars and >= 1,000 forks 2. 5,000 >= stars < 10,000, and 750 >= forks < 1,000 3. 1,000 >= stars < 5,000 and 500 >= forks < 750 4. 1,000 > stars and 250 > forks and in Java

7. Is/Are the dataset(s) publicly available online? If yes, please indicate their name and links.

Dataset is not available

8. Is the application context (dataset or application domain) different from that for which the technique was originally designed?

Yes, NRC is general purpose

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?

No

12. Write down any other comments/notes here.

No