Recommending insightful comments for source code using crowdsourced knowledge

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)?

Stanford Sentiment Analyzer of R. Socher, A. Perelygin, J. Y. Wu, J. Chuang, C. D. Manning, A. Y. Ng, and C. Potts. Recursive Deep Models for Semantic Compositionality over a Sentiment Treebank. In Proc. EMNLP, 2013.

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.

Stanford Sentiment Analyzer http://nlp.stanford.edu:8080/sentiment/rntnDemo.html

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

Identification of insightful comments

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

Sentiment of Stack Overflow comments

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

5,039 discussion comments targeting 292 code segments, and 706 gold comments (i.e., manually labeled) (Table I) from three popular domains– Java, Android and C#

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

http://www.usask.ca/∼masud.rahman/codeinsight Unfortunately the website is offline

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

Yes. the original technique was designed based on 215,154 unique phrases extracted from movie reviews

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.

N/A