type:: [[REVIEWS]]
tags:: 
year:: 2023
venue:: [[ASE]]
full-title:: Towards Robustness of Deep Program Processing Models – Detection, Estimation and Enhancement
date-start:: [[26-06-2023]] - 18:53
date-submitted::
external-links:: [#35 - ASE'23 Journal-First (hotcrp.com)](https://ase2023-jf.hotcrp.com/review/35)
status:: [[DONE]] 
deadline-submission:: [[26-06-2023]] 
file:: ![ase2023-jf-paper35.pdf](../assets/ase2023-jf-paper35_1687798421651_0.pdf)

	- [[Highlights]]
	- [[Comments]]
		- The paper introduces CARROT, an approach designed to evaluate and enhance the robustness of deep learning (DL) models in the domain of source code processing.
		- The paper aligns with the focus of the ASE conference. It does not present a secondary study, and the CARROT approach has not been previously described by the authors. Therefore, the paper contributes original research.
		- There is no given evidence to confirm that the paper has not been submitted to journal-first programs in 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.