VUDDY: A Scalable Approach for Vulnerable Code Clone Discovery

226 indexed citations

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This paper, published in 2017, received 226 indexed citations. Written by Seulbae Kim, Seunghoon Woo, Heejo Lee and Hakjoo Oh covering the research area of Software, Signal Processing and Information Systems. It is primarily cited by scholars working on Information Systems (208 citations), Signal Processing (170 citations) and Software (140 citations). Published in .

Countries where authors are citing VUDDY: A Scalable Approach for Vulnerable Code Clone Discovery

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Citations

This map shows the geographic impact of VUDDY: A Scalable Approach for Vulnerable Code Clone Discovery. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by VUDDY: A Scalable Approach for Vulnerable Code Clone Discovery with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites VUDDY: A Scalable Approach for Vulnerable Code Clone Discovery more than expected).

Fields of papers citing VUDDY: A Scalable Approach for Vulnerable Code Clone Discovery

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Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of VUDDY: A Scalable Approach for Vulnerable Code Clone Discovery. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the VUDDY: A Scalable Approach for Vulnerable Code Clone Discovery.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

This paper is also available at doi.org/10.1109/sp.2017.62.

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