Natural language insights from code reviews that missed a vulnerability
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)?
Naıve Bayes classifier with lemmas of the words from code reviews as features and their corresponding TF-IDF as values
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 characterize the linguistic features that contribute to the likelihood that a code review has missed a vulnerability
5. What the researchers want to achieve by applying the technique(s) (e.g., calculate the sentiment polarity of app reviews)?
to distinguish between neutral and missed vulnerability code reviews
6. Which dataset(s) the technique is applied on?
all code reviews (2008–2016) for the Chromium project
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?
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?
10-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?
precision, recall, and F-measure
12. Write down any other comments/notes here.
-