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Variability extraction and modeling for product variants

Overview of attention for article published in Software and Systems Modeling, January 2016
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Title
Variability extraction and modeling for product variants
Published in
Software and Systems Modeling, January 2016
DOI 10.1007/s10270-015-0512-y
Pubmed ID
Authors

Lukas Linsbauer, Roberto Erick Lopez-Herrejon, Alexander Egyed

Abstract

Fast-changing hardware and software technologies in addition to larger and more specialized customer bases demand software tailored to meet very diverse requirements. Software development approaches that aim at capturing this diversity on a single consolidated platform often require large upfront investments, e.g., time or budget. Alternatively, companies resort to developing one variant of a software product at a time by reusing as much as possible from already-existing product variants. However, identifying and extracting the parts to reuse is an error-prone and inefficient task compounded by the typically large number of product variants. Hence, more disciplined and systematic approaches are needed to cope with the complexity of developing and maintaining sets of product variants. Such approaches require detailed information about the product variants, the features they provide and their relations. In this paper, we present an approach to extract such variability information from product variants. It identifies traces from features and feature interactions to their implementation artifacts, and computes their dependencies. This work can be useful in many scenarios ranging from ad hoc development approaches such as clone-and-own to systematic reuse approaches such as software product lines. We applied our variability extraction approach to six case studies and provide a detailed evaluation. The results show that the extracted variability information is consistent with the variability in our six case study systems given by their variability models and available product variants.

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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.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 50 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 11 22%
Student > Ph. D. Student 9 18%
Student > Doctoral Student 6 12%
Researcher 3 6%
Student > Bachelor 2 4%
Other 6 12%
Unknown 13 26%
Readers by discipline Count As %
Computer Science 29 58%
Engineering 5 10%
Business, Management and Accounting 2 4%
Agricultural and Biological Sciences 1 2%
Sports and Recreations 1 2%
Other 1 2%
Unknown 11 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 02 March 2016.
All research outputs
#16,069,695
of 23,849,058 outputs
Outputs from Software and Systems Modeling
#343
of 721 outputs
Outputs of similar age
#238,618
of 401,124 outputs
Outputs of similar age from Software and Systems Modeling
#3
of 10 outputs
Altmetric has tracked 23,849,058 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 721 research outputs from this source. They receive a mean Attention Score of 2.2. This one is in the 39th percentile – i.e., 39% 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 401,124 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than 7 of them.