Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
The Daikon system for dynamic detection of likely invariants
2007631 citationsMichael D. Ernst, Jeff Perkins et al.Science of Computer Programmingprofile →
Information-Flow Analysis of Android Applications in DroidSafe
2015301 citationsMichael I. Gordon, Deokhwan Kim et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Jeff Perkins's research. 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 Jeff Perkins with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jeff Perkins more than expected).
This network shows the impact of papers produced by Jeff Perkins. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jeff Perkins. The network helps show where Jeff Perkins may publish in the future.
Co-authorship network of co-authors of Jeff Perkins
This figure shows the co-authorship network connecting the top 25 collaborators of Jeff Perkins.
A scholar is included among the top collaborators of Jeff Perkins based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Jeff Perkins. Jeff Perkins is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Gordon, Michael I., et al.. (2019). Precise and Comprehensive Provenance Tracking for Android Devices. DSpace@MIT (Massachusetts Institute of Technology).3 indexed citations
3.
Gordon, Michael I., et al.. (2015). Information-Flow Analysis of Android Applications in DroidSafe.301 indexed citations breakdown →
4.
Coglio, Alessandro, Stephen Fitzpatrick, Cordell Green, et al.. (2015). Vulnerabilities in Bytecode Removed by Analysis, Nuanced Confinement and Diversification (VIBRANCE).1 indexed citations
Perkins, Jeff, Sung Hun Kim, Saman Amarasinghe, et al.. (2009). Self-defending software: Automatically patching security vulnerabilities.1 indexed citations
Ernst, Michael D., Jeff Perkins, Philip J. Guo, et al.. (2007). The Daikon system for dynamic detection of likely invariants. Science of Computer Programming. 69(1-3). 35–45.631 indexed citations breakdown →
10.
Perkins, Jeff, et al.. (2007). Pluggable type-checking for custom type qualifiers in Java. DSpace@MIT (Massachusetts Institute of Technology).2 indexed citations
11.
Artzi, Shay, Michael D. Ernst, Adam Kieżun, Carlos Pacheco, & Jeff Perkins. (2006). Finding the needles in the haystack: Generating legal test inputs for object-oriented programs. DSpace@MIT (Massachusetts Institute of Technology).32 indexed citations
12.
Guo, Philip J., Jeff Perkins, Stephen McCamant, & Michael D. Ernst. (2006). Dynamic inference of abstract types. 255–265.49 indexed citations
Perkins, Jeff, et al.. (1997). Teach Yourself SQL in 21 Days.4 indexed citations
19.
Perkins, Jeff, et al.. (1996). Teach yourself ActiveX programming in 21 days.
20.
Perkins, Jeff, et al.. (1995). Teach Yourself ODBC in 21 Days.
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.