| Title |
Reengineering legacy applications into software product lines: a systematic mapping
|
|---|---|
| Published in |
Empirical Software Engineering, February 2017
|
| DOI | 10.1007/s10664-017-9499-z |
| Authors |
Wesley K. G. Assunção, Roberto E. Lopez-Herrejon, Lukas Linsbauer, Silvia R. Vergilio, Alexander Egyed |
X Demographics
The data shown below were collected from the profiles of 4 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 | 1 | 25% |
| Spain | 1 | 25% |
| United States | 1 | 25% |
| Unknown | 1 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 50% |
| Scientists | 1 | 25% |
| Science communicators (journalists, bloggers, editors) | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 106 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 106 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 25 | 24% |
| Student > Ph. D. Student | 18 | 17% |
| Student > Bachelor | 11 | 10% |
| Professor > Associate Professor | 7 | 7% |
| Student > Doctoral Student | 5 | 5% |
| Other | 14 | 13% |
| Unknown | 26 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 60 | 57% |
| Engineering | 10 | 9% |
| Business, Management and Accounting | 4 | 4% |
| Economics, Econometrics and Finance | 2 | 2% |
| Psychology | 2 | 2% |
| Other | 3 | 3% |
| Unknown | 25 | 24% |
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 17 May 2023.
All research outputs
#13,210,128
of 23,791,297 outputs
Outputs from Empirical Software Engineering
#318
of 729 outputs
Outputs of similar age
#197,344
of 423,919 outputs
Outputs of similar age from Empirical Software Engineering
#11
of 18 outputs
Altmetric has tracked 23,791,297 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 729 research outputs from this source. They receive a mean Attention Score of 4.7. This one has gotten more attention than average, scoring higher than 55% 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 423,919 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 52% of its contemporaries.
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 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.