Automated classification of software issue reports using machine learning techniques: an empirical study

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)?

Machine learning on the text of issues is used to classify the issue type as bug or not.

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 classify issue reports as bug or non-bug

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

Classify the type of bug report

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

Issue reports of three open source projects

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

Yes, they reuse the dataset from: It’snotabug,it’safeature:how misclassification impacts bug prediction.

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?

F-Measure and Accuracy computed through k-fold cross-validation

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?

F-Measure and Accuracy

12. Write down any other comments/notes here.

This is borderline, it concerns the automatic classification of issues. Similar to classifying app reviews, but these are not really opinions.