| Title |
On the correctness of electronic documents: studying, finding, and localizing inconsistency bugs in PDF readers and files
|
|---|---|
| Published in |
Empirical Software Engineering, March 2018
|
| DOI | 10.1007/s10664-018-9600-2 |
| Authors |
Tomasz Kuchta, Thibaud Lutellier, Edmund Wong, Lin Tan, Cristian Cadar |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 3 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Science communicators (journalists, bloggers, editors) | 1 | 33% |
| Members of the public | 1 | 33% |
| Scientists | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 16 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 6 | 38% |
| Researcher | 3 | 19% |
| Student > Ph. D. Student | 1 | 6% |
| Professor > Associate Professor | 1 | 6% |
| Student > Postgraduate | 1 | 6% |
| Other | 0 | 0% |
| Unknown | 4 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 7 | 44% |
| Engineering | 2 | 13% |
| Economics, Econometrics and Finance | 1 | 6% |
| Nursing and Health Professions | 1 | 6% |
| Unknown | 5 | 31% |
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 11 June 2019.
All research outputs
#13,016,439
of 23,045,021 outputs
Outputs from Empirical Software Engineering
#340
of 709 outputs
Outputs of similar age
#160,443
of 332,343 outputs
Outputs of similar age from Empirical Software Engineering
#16
of 34 outputs
Altmetric has tracked 23,045,021 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 709 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 50% 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 332,343 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 51% of its contemporaries.
We're also able to compare this research output to 34 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 50% of its contemporaries.