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The artifact for this paper contains tools and data to reproduce, with minimal effort, the entire testing flow and corroborate its claims. All results can be generated from scratch (source codes) and run across different platforms with the provided docker image. The pre-built docker image supports runs across different platforms with software dependencies taken care of, including a pre-compiled copy of the proposed decompiler, its variants, state-of-the-art decompilers used for comparison, and miscellaneous software such as vim and python. We provide an easy top level interface, \textit{artifact.py} to simplify the testing process.
Automatic Parallelization, Decompiler, ASPLOS'23
Automatic Parallelization, Decompiler, ASPLOS'23
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 1 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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| downloads | 1 |

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Downloads provided by UsageCounts