Analyzing reviews and code of mobile apps for better release planning
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)?
Gradient Boosted Regression Trees (GBRT)
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.
GBRT implemented in the Graphlab library
4. What is the main goal of the whole study?
to define a multi-level taxonomy for user reviews targeted towards mobile specific issues, and propose a URR prototype that is able to organize reviews according to the defined taxonomy and recommend the source code files that are likely to be modified to handle the mobile specific issues and requests highlighted by the users
5. What the researchers want to achieve by applying the technique(s) (e.g., calculate the sentiment polarity of app reviews)?
to automatically classify reviews according to taxonomy of mobile specific categories and recommend for a particular review what are the source code files that need to be modified to handle the issue described in the user review
6. Which dataset(s) the technique is applied on?
user reviews and code of 39 mobile applications
7. Is/Are the dataset(s) publicly available online? If yes, please indicate their name and links.
link invalid
8. Is the application context (dataset or application domain) different from that for which the technique was originally designed?
retrained
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?
ability to organize reviews according to meaningful and actionable maintenance and evolution tasks for developers: a quantitative evaluation for measuring the precision, recall, and the F1 score obtained by the approach while classifying reviews according to the high and low level taxonomy
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.
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