1.7 KiB
1.7 KiB
type:: REVIEWS
tags:: #userfeedback #pretrainedmodels
year:: 2023
venue:: ASE
full-title:: Evaluating Pre-Trained Models for User Feedback Analysis in Software Engineering: A Study on Classification of App-Reviews
date-start:: 27-06-2023 - 14:48
date-submitted:: 27-06-2023
external-links:: #98 - ASE'23 Journal-First (hotcrp.com)
status:: DONE
deadline-submission:: 27-06-2023
file::
- [[Highlights]]
- [[Comments]]
- This is an original work, which evaluates the usage of Pretrained Language Models for issue classification from app reviews in multiple settings and compares them with the existing models. The paper is in the scope of the ASE conference.
- It is not evident if the paper is under consideration for journal-first programs of other conferences.
- CRITERIA
- The paper is in the scope of the conference, as defined in the call
for ASE 2023 research papers
- The paper does not exclusively report a secondary study, e.g.,
systematic reviews, mapping studies, surveys.
- The paper reports completely new research results and/or presents novel contributions that significantly extend and were not previously reported in prior work. As a rough guide, a journal-first paper should have at least 70% new content over and above the content of previous publications. As such, the expectation is that an extension of a full 8-10 page conference or workshop paper would not be deemed a journal-first paper.
- The paper has not been presented at, and is not under consideration for, journal-first programs of other conferences.