| 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 |
X Demographics
The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Sweden | 3 | 43% |
| United States | 2 | 29% |
| India | 1 | 14% |
| Russia | 1 | 14% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 5 | 71% |
| Members of the public | 2 | 29% |
Mendeley readers
The data shown below were compiled from readership statistics for 138 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| New Zealand | 1 | <1% |
| Spain | 1 | <1% |
| Brazil | 1 | <1% |
| Unknown | 135 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 40 | 29% |
| Student > Ph. D. Student | 24 | 17% |
| Student > Postgraduate | 7 | 5% |
| Student > Bachelor | 7 | 5% |
| Student > Doctoral Student | 6 | 4% |
| Other | 22 | 16% |
| Unknown | 32 | 23% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 75 | 54% |
| Engineering | 11 | 8% |
| Social Sciences | 3 | 2% |
| Business, Management and Accounting | 3 | 2% |
| Agricultural and Biological Sciences | 1 | <1% |
| Other | 6 | 4% |
| Unknown | 39 | 28% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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
#8,059,753
of 26,017,215 outputs
Outputs from Empirical Software Engineering
#255
of 803 outputs
Outputs of similar age
#88,269
of 282,831 outputs
Outputs of similar age from Empirical Software Engineering
#3
of 11 outputs
Altmetric has tracked 26,017,215 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 803 research outputs from this source. They receive a mean Attention Score of 4.8. This one has gotten more attention than average, scoring higher than 67% 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 282,831 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.
We're also able to compare this research output to 11 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 72% of its contemporaries.