Information Recommendation Based on Domain Knowledge in App Descriptions for Improving the Quality of Requirements
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)?
Information Recommendation based on DRDM (Data-based Raw Domain Model)
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 extract and recommend requirements related information from App descriptions
5. What the researchers want to achieve by applying the technique(s) (e.g., calculate the sentiment polarity of app reviews)?
same as 4
6. Which dataset(s) the technique is applied on?
574 Apps from 5 categories on Google Play
7. Is/Are the dataset(s) publicly available online? If yes, please indicate their name and links.
No
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?
effectiveness: constructed a DRDM from the descriptions of the 29 Apps and used it to identify a set of Apps satisfying it. The results were compared with the standard labels in the truth set usefulness: survey with developers
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?
effective: precision, recall, f1
12. Write down any other comments/notes here.
-