Confusion detection in code reviews

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)?

confusion detector based on classifiers

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.

partially, the features used in classifiers are available https://github.com/spgroup/confusion

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

to identify confusion in developers’ comments

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

to identify confusion in developers’ comments

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

396 general and 396 inline comments with labels indicating whether the comment contains confusion as gold standard set

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

https://github.com/spgroup/confusion

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. the performance of different classifiers are compared against the ZeroR, which always predicts the majority class, and random guessing in a 10-fold cross-validation setting The best precision on confusion class is obtained by OneR

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, f1

12. Write down any other comments/notes here.

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