A network-centric approach for estimating trust between open source software developers

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)?

An XGBoost regression that is explained in the paper.

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.

All work of the paper is available: https://doi.org/10.5281/zenodo.3522461

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

Estimate trust between open-source developers.

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

Estimate how a reviewer evaluates the contributions by an author, on a scale from very negative to very positive.

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

A hand labeled set of 616 pull-request comments of 179 Python projects on GitHub. The python projects have been selected for using pull-requests on GitHub.

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

Yes: https://doi.org/10.5281/zenodo.3522461

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

No.

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?

Yes, models are verified using the Mean Absolute Error. MAE of their chosen technique is roughly .75.

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?

Mean Absolute Error.

12. Write down any other comments/notes here.

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