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Model-Based Test Oracle Generation for Automated Unit Testing of Agent Systems

Overview of attention for article published in IEEE Transactions on Software Engineering, August 2013
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About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Above-average Attention Score compared to outputs of the same age and source (52nd percentile)

Mentioned by

patent
1 patent

Citations

dimensions_citation
36 Dimensions

Readers on

mendeley
77 Mendeley
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Title
Model-Based Test Oracle Generation for Automated Unit Testing of Agent Systems
Published in
IEEE Transactions on Software Engineering, August 2013
DOI 10.1109/tse.2013.10
Authors

L. Padgham, Zhiyong Zhang, J. Thangarajah, T. Miller

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 77 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 1%
Spain 1 1%
Portugal 1 1%
Brazil 1 1%
Unknown 73 95%

Demographic breakdown

Readers by professional status Count As %
Student > Master 21 27%
Student > Ph. D. Student 14 18%
Researcher 7 9%
Student > Bachelor 6 8%
Professor 5 6%
Other 12 16%
Unknown 12 16%
Readers by discipline Count As %
Computer Science 54 70%
Engineering 8 10%
Social Sciences 1 1%
Immunology and Microbiology 1 1%
Unknown 13 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 01 October 2019.
All research outputs
#8,535,684
of 25,377,790 outputs
Outputs from IEEE Transactions on Software Engineering
#2,238
of 6,368 outputs
Outputs of similar age
#71,915
of 212,171 outputs
Outputs of similar age from IEEE Transactions on Software Engineering
#13
of 40 outputs
Altmetric has tracked 25,377,790 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,368 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 27th percentile – i.e., 27% 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 212,171 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 40 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.