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Automatic identifier inconsistency detection using code dictionary

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

facebook
1 Facebook page

Citations

dimensions_citation
28 Dimensions

Readers on

mendeley
41 Mendeley
Title
Automatic identifier inconsistency detection using code dictionary
Published in
Empirical Software Engineering, March 2015
DOI 10.1007/s10664-015-9369-5
Authors

Suntae Kim, Dongsun Kim

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 41 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 20%
Student > Master 8 20%
Student > Doctoral Student 3 7%
Researcher 3 7%
Student > Postgraduate 3 7%
Other 5 12%
Unknown 11 27%
Readers by discipline Count As %
Computer Science 24 59%
Mathematics 1 2%
Nursing and Health Professions 1 2%
Earth and Planetary Sciences 1 2%
Social Sciences 1 2%
Other 1 2%
Unknown 12 29%
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 18 April 2016.
All research outputs
#20,322,106
of 22,865,319 outputs
Outputs from Empirical Software Engineering
#624
of 705 outputs
Outputs of similar age
#218,433
of 258,855 outputs
Outputs of similar age from Empirical Software Engineering
#11
of 23 outputs
Altmetric has tracked 22,865,319 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 705 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 258,855 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 23 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.