| Title |
Genetic Algorithm-based Test Generation for Software Product Line with the Integration of Fault Localization Techniques
|
|---|---|
| Published in |
Empirical Software Engineering, February 2017
|
| DOI | 10.1007/s10664-016-9494-9 |
| Authors |
Xuelin Li, W. Eric Wong, Ruizhi Gao, Linghuan Hu, Shigeru Hosono |
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.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 50% |
| Science communicators (journalists, bloggers, editors) | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Belgium | 1 | 2% |
| Brazil | 1 | 2% |
| Unknown | 40 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 8 | 19% |
| Student > Ph. D. Student | 6 | 14% |
| Student > Doctoral Student | 5 | 12% |
| Student > Bachelor | 5 | 12% |
| Lecturer | 3 | 7% |
| Other | 3 | 7% |
| Unknown | 12 | 29% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 26 | 62% |
| Engineering | 4 | 10% |
| Chemistry | 1 | 2% |
| Agricultural and Biological Sciences | 1 | 2% |
| Unknown | 10 | 24% |
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 April 2018.
All research outputs
#14,967,526
of 23,025,074 outputs
Outputs from Empirical Software Engineering
#485
of 707 outputs
Outputs of similar age
#243,450
of 421,056 outputs
Outputs of similar age from Empirical Software Engineering
#15
of 18 outputs
Altmetric has tracked 23,025,074 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 421,056 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one is in the 11th percentile – i.e., 11% of its contemporaries scored the same or lower than it.