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Studying just-in-time defect prediction using cross-project models

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

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1 Facebook page

Citations

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171 Dimensions

Readers on

mendeley
117 Mendeley
Title
Studying just-in-time defect prediction using cross-project models
Published in
Empirical Software Engineering, September 2015
DOI 10.1007/s10664-015-9400-x
Authors

Yasutaka Kamei, Takafumi Fukushima, Shane McIntosh, Kazuhiro Yamashita, Naoyasu Ubayashi, Ahmed E. Hassan

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 117 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Canada 1 <1%
Unknown 116 99%

Demographic breakdown

Readers by professional status Count As %
Student > Master 26 22%
Student > Ph. D. Student 17 15%
Student > Bachelor 8 7%
Student > Doctoral Student 7 6%
Researcher 6 5%
Other 24 21%
Unknown 29 25%
Readers by discipline Count As %
Computer Science 71 61%
Engineering 6 5%
Economics, Econometrics and Finance 2 2%
Agricultural and Biological Sciences 1 <1%
Energy 1 <1%
Other 1 <1%
Unknown 35 30%
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 23 November 2016.
All research outputs
#20,355,479
of 22,903,988 outputs
Outputs from Empirical Software Engineering
#624
of 706 outputs
Outputs of similar age
#225,649
of 268,695 outputs
Outputs of similar age from Empirical Software Engineering
#5
of 12 outputs
Altmetric has tracked 22,903,988 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 268,695 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 12 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.