logseq/pages/ase2023-jf-paper35.md

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) status:: DONE deadline-submission:: 26-06-2023 file::

- [[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.
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- 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.