TY - JOUR
T1 - Learning Contextual-Variability Models
AU - Temple, Paul
AU - Acher, Mathieu
AU - Jézéquel, Jean-Marc
AU - Barais, Olivier
PY - 2017/11/13
Y1 - 2017/11/13
N2 - Modeling how contextual factors relate to a software system's configuration space is usually a manual, error-prone task that depends highly on expert knowledge. Machine-learning techniques can automatically predict the acceptable software configurations for a given context. Such an approach executes and observes a sample of software configurations within a sample of contexts. It then learns what factors of each context will likely discard or activate some of the software's features. This lets developers and product managers automatically extract the rules that specialize highly configurable systems for specific contexts.
AB - Modeling how contextual factors relate to a software system's configuration space is usually a manual, error-prone task that depends highly on expert knowledge. Machine-learning techniques can automatically predict the acceptable software configurations for a given context. Such an approach executes and observes a sample of software configurations within a sample of contexts. It then learns what factors of each context will likely discard or activate some of the software's features. This lets developers and product managers automatically extract the rules that specialize highly configurable systems for specific contexts.
KW - contextual variability
KW - contextual-variability modeling
KW - machine learning
KW - software development
KW - software engineering
UR - https://hal.inria.fr/hal-01659137
UR - http://www.scopus.com/inward/record.url?scp=85038108201&partnerID=8YFLogxK
U2 - 10.1109/MS.2017.4121211
DO - 10.1109/MS.2017.4121211
M3 - Article
SN - 0740-7459
VL - 34
SP - 64
EP - 70
JO - IEEE Software
JF - IEEE Software
IS - 6
M1 - 8106868
ER -