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Automated bug assignment: Ensemble-based machine learning in large scale industrial contexts

Overview of attention for article published in Empirical Software Engineering, September 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (77th percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

Mentioned by

twitter
7 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
25 Dimensions

Readers on

mendeley
72 Mendeley
Title
Automated bug assignment: Ensemble-based machine learning in large scale industrial contexts
Published in
Empirical Software Engineering, September 2015
DOI 10.1007/s10664-015-9401-9
Authors

Leif Jonsson, Markus Borg, David Broman, Kristian Sandahl, Sigrid Eldh, Per Runeson

Twitter Demographics

The data shown below were collected from the profiles of 7 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 72 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 1%
Brazil 1 1%
New Zealand 1 1%
Unknown 69 96%

Demographic breakdown

Readers by professional status Count As %
Student > Master 27 38%
Student > Ph. D. Student 12 17%
Unspecified 9 13%
Student > Postgraduate 6 8%
Student > Doctoral Student 4 6%
Other 14 19%
Readers by discipline Count As %
Computer Science 45 63%
Unspecified 14 19%
Engineering 7 10%
Social Sciences 2 3%
Business, Management and Accounting 1 1%
Other 3 4%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 17 August 2018.
All research outputs
#3,009,495
of 13,384,384 outputs
Outputs from Empirical Software Engineering
#86
of 462 outputs
Outputs of similar age
#53,055
of 241,079 outputs
Outputs of similar age from Empirical Software Engineering
#1
of 8 outputs
Altmetric has tracked 13,384,384 research outputs across all sources so far. Compared to these this one has done well and is in the 77th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 462 research outputs from this source. They receive a mean Attention Score of 3.8. This one has done well, scoring higher than 81% of its peers.
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 241,079 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 77% of its contemporaries.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them