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Found 4 papers, 3 papers with code
Revisiting Neural Program Smoothing for Fuzzing
In this paper, we conduct the most extensive evaluation of NPS fuzzers against standard gray-box fuzzers (>11 CPU years and >5. 5 GPU years), and make the following contributions: (1) We find that the original performance claims for NPS fuzzers do not hold; a gap we relate to fundamental, implementation, and experimental limitations of prior works.
Restoring Execution Environments of Jupyter Notebooks
More than ninety percent of published Jupyter notebooks do not state dependencies on external packages.
Software Engineering
Building Fast Fuzzers
Random fuzzers are very efficient, as they can quickly generate random inputs; but they are not very effective, as the large majority of inputs generated is syntactically invalid.
Software Engineering Cryptography and Security Programming Languages D.4.6; D.2.5
Localizing Bugs in Program Executions with Graphical Models
We devise a graphical model that supports the process of debugging software by guiding developers to code that is likely to contain defects.
