Talks and Poster Presentations (with Proceedings-Entry):
H. Kaindl, S. Kramer, R. Hoch:
"An Inductive Learning Perspective on Automated Generation of Feature Models from Given Product Specifications";
Talk: 22nd International Systems and Software Product Line Conference,
Gothenburg;
09-10-2018
- 09-14-2018; in: "22nd International Systems and Software Product Line Conference - Proceedings",
ACM,
Volume 1
(2018),
ISBN: 978-1-4503-6464-5;
25
- 31.
English abstract:
For explicit representation of commonality and variability of a
product line, a feature model is mostly used. An open question is
how a feature model can be inductively learned in an automated
way from a limited number of given product specifications in terms
of features.
We propose to address this problem through machine learning,
more precisely inductive generalization from examples. However,
no counter-examples are assumed to exist. Basically, a feature model
needs to be complete with respect to all the given example specifications.
First results indicate the feasibility of this approach, even
for generating hierarchies, but many open challenges remain.
Keywords:
Generating feature models, machine learning, inductive generalization from examples
"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)
http://dx.doi.org/10.1145/3233027.3233031
Created from the Publication Database of the Vienna University of Technology.