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An automatic method for assessing the versions affected by a vulnerability

Overview of attention for article published in Empirical Software Engineering, December 2015
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Mentioned by

facebook
1 Facebook page

Citations

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34 Dimensions

Readers on

mendeley
40 Mendeley
Title
An automatic method for assessing the versions affected by a vulnerability
Published in
Empirical Software Engineering, December 2015
DOI 10.1007/s10664-015-9408-2
Authors

Viet Hung Nguyen, Stanislav Dashevskyi, Fabio Massacci

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 40 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 33%
Student > Master 10 25%
Professor 3 8%
Student > Doctoral Student 3 8%
Other 2 5%
Other 1 3%
Unknown 8 20%
Readers by discipline Count As %
Computer Science 25 63%
Business, Management and Accounting 1 3%
Biochemistry, Genetics and Molecular Biology 1 3%
Social Sciences 1 3%
Engineering 1 3%
Other 0 0%
Unknown 11 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 January 2017.
All research outputs
#20,382,391
of 22,931,367 outputs
Outputs from Empirical Software Engineering
#625
of 706 outputs
Outputs of similar age
#326,748
of 389,153 outputs
Outputs of similar age from Empirical Software Engineering
#4
of 13 outputs
Altmetric has tracked 22,931,367 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 706 research outputs from this source. They receive a mean Attention Score of 4.8. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 389,153 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 13 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.