To Do or Not To Do: Distill crowdsourced negative caveats to augment api documentation

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)?

Stanford Parser combined with post-hoc processing of negations. From the paper: 'Among syntactic roles, we can use negation modifier (that is, neg) to detect negative expressions. [...] To ensure the negative expressions are on APIs, we select only negative sentences whose subject or object is a given API. For example, both sentences “JSONObject does not have too much additional overhead on top of a HashMap” and “HashMap doesn't define the order of iteration over the elements” are negative sentences and both mention HashMap. Only the second sentence is selected as a candidate sentence for HashMap because the negative expression is on the API. More specifically, a given API must exist in nsubj or dobj syntactic role in a sentence.'

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 augment the API documentation with crowdsourced negative caveats.

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

The authors propose Disca, a novel approach for automatically Distilling desirable API negative caveats from unstructured Q&A discussions. Through sentence selection and prominent term clustering, Disca ensures that distilled caveats are context‐independent, prominent, semantically diverse, and nonredundant.

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

Stack Overflow questions

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

https://github.com/IRNLPCoder/CaveatDataSet

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

NA

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?

The sentiment analysis is not evaluated.

12. Write down any other comments/notes here.

-