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Usage and attribution of Stack Overflow code snippets in GitHub projects

Overview of attention for article published in Empirical Software Engineering, October 2018
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#29 of 747)
  • High Attention Score compared to outputs of the same age (89th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

news
1 news outlet
twitter
10 X users
facebook
1 Facebook page
q&a
1 Q&A thread

Citations

dimensions_citation
62 Dimensions

Readers on

mendeley
49 Mendeley
Title
Usage and attribution of Stack Overflow code snippets in GitHub projects
Published in
Empirical Software Engineering, October 2018
DOI 10.1007/s10664-018-9650-5
Authors

Sebastian Baltes, Stephan Diehl

X Demographics

X Demographics

The data shown below were collected from the profiles of 10 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 49 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 20%
Student > Master 7 14%
Student > Doctoral Student 4 8%
Professor > Associate Professor 4 8%
Student > Bachelor 3 6%
Other 7 14%
Unknown 14 29%
Readers by discipline Count As %
Computer Science 27 55%
Environmental Science 1 2%
Arts and Humanities 1 2%
Physics and Astronomy 1 2%
Social Sciences 1 2%
Other 0 0%
Unknown 18 37%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 20. 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 28 August 2023.
All research outputs
#1,707,803
of 24,340,143 outputs
Outputs from Empirical Software Engineering
#29
of 747 outputs
Outputs of similar age
#36,688
of 347,708 outputs
Outputs of similar age from Empirical Software Engineering
#1
of 20 outputs
Altmetric has tracked 24,340,143 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 747 research outputs from this source. They receive a mean Attention Score of 4.8. This one has done particularly well, scoring higher than 96% 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 347,708 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 89% of its contemporaries.
We're also able to compare this research output to 20 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 99% of its contemporaries.