25 lines
1.8 KiB
Markdown
25 lines
1.8 KiB
Markdown
type:: [[REVIEWS]]
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tags::
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year:: 2023
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venue:: [[ASE]]
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full-title:: Towards Robustness of Deep Program Processing Models – Detection, Estimation and Enhancement
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date-start:: [[26-06-2023]] - 18:53
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date-submitted::
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external-links:: [#35 - ASE'23 Journal-First (hotcrp.com)](https://ase2023-jf.hotcrp.com/review/35)
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status:: [[DONE]]
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deadline-submission:: [[26-06-2023]]
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file:: 
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- [[Highlights]]
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- [[Comments]]
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- 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.
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- 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.
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- 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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-
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- CRITERIA
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- The paper is in the scope of the conference, as defined in the call
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for ASE 2023 research papers
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- The paper does not exclusively report a secondary study, e.g.,
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systematic reviews, mapping studies, surveys.
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- 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.
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- The paper has not been presented at, and is not under consideration for, journal-first programs of other conferences. |