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Towards just-in-time suggestions for log changes

Overview of attention for article published in Empirical Software Engineering, October 2016
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About this Attention Score

  • Average Attention Score compared to outputs of the same age

Mentioned by

twitter
2 X users
facebook
1 Facebook page

Citations

dimensions_citation
59 Dimensions

Readers on

mendeley
45 Mendeley
Title
Towards just-in-time suggestions for log changes
Published in
Empirical Software Engineering, October 2016
DOI 10.1007/s10664-016-9467-z
Authors

Heng Li, Weiyi Shang, Ying Zou, Ahmed E. Hassan

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 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 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 45 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 13 29%
Student > Ph. D. Student 12 27%
Researcher 4 9%
Student > Bachelor 4 9%
Professor > Associate Professor 2 4%
Other 3 7%
Unknown 7 16%
Readers by discipline Count As %
Computer Science 26 58%
Engineering 8 18%
Social Sciences 2 4%
Business, Management and Accounting 2 4%
Unknown 7 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 October 2017.
All research outputs
#14,952,935
of 22,999,744 outputs
Outputs from Empirical Software Engineering
#486
of 707 outputs
Outputs of similar age
#188,633
of 314,417 outputs
Outputs of similar age from Empirical Software Engineering
#25
of 28 outputs
Altmetric has tracked 22,999,744 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 707 research outputs from this source. They receive a mean Attention Score of 4.8. This one is in the 28th percentile – i.e., 28% 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 314,417 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 28 others from the same source and published within six weeks on either side of this one. This one is in the 3rd percentile – i.e., 3% of its contemporaries scored the same or lower than it.