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file:: [SATToSE_2023_paper_3_1683215144921_0.pdf](../assets/SATToSE_2023_paper_3_1683215144921_0.pdf)
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file-path:: ../assets/SATToSE_2023_paper_3_1683215144921_0.pdf
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- available tools for vulnerability detection, practitioners are puzzled when selecting the most suitable approach to adopt in the projects they work on
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hl-page:: 1
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hl-color:: purple
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id:: 6453d72a-355e-49c8-a1cd-dfc78228398a
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- annot be easily compared
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ls-type:: annotation
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hl-page:: 1
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hl-color:: purple
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id:: 6453d72e-9f2d-44e7-8246-a4f5d08aa777
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- lack of a detailed comparison among existing approaches for vulnerability detection
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ls-type:: annotation
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hl-page:: 1
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hl-color:: purple
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id:: 6453d739-ce48-4f60-bdea-53323e07110a
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- new way to compare the tools with each other
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ls-type:: annotation
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hl-page:: 1
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hl-color:: purple
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id:: 6453d73f-dc40-4b11-9ef2-e07c5b4f4e0c
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- . By performing a multivocal literature review, we plan to gather information about the existing tools for vulnerability detection and the related benchmark
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hl-page:: 1
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hl-color:: purple
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id:: 6453d74d-01b5-4c36-820d-7e07a48d2199
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- How do vulnerability detection tools perform under different benchmarks in terms of detection rates, analysis rates, and output understandability
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hl-page:: 4
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hl-color:: purple
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id:: 6453d766-87af-460e-81eb-212f7ec13497
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- the winners of each of the three categories—i.e., static analyzers, dynamic analyzers, and machine-learning models—will be compared in a “final” bout that will decree the winner on that benchmark
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ls-type:: annotation
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hl-page:: 5
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id:: 6453d7aa-f4f2-4827-8e44-8301495c66c8
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- performance metrics
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hl-page:: 5
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hl-color:: purple
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id:: 6453d81c-957e-49ed-a68e-25096b827aaf
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- wide range of metrics commonly adopted in literature for evaluating these kinds of tools, measuring aspects like (i) the discovery rate of vulnerabilities, (ii) the rate of analyzed components (e.g., code elements) per time unit, and (iii) the understandability of the output
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ls-type:: annotation
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hl-page:: 5
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hl-color:: purple
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id:: 6453d829-e088-42aa-8de3-9a8981634de2
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