Paper Info
Title
Learning By Sampling: Learning Behavioral Family Models From Software Product Lines
Abstract
Family-based behavioral analysis operates on a single specification artifact, referred to as family model, annotated with feature constraints to express behavioral variability in terms of conditional states and transitions. Family-based behavioral modeling paves the way for efficient model-based analysis of software product lines. Family-based behavioral model learning incorporates feature model analysis and model learning principles to efficiently unify product models into a family model and integrate the behavior of various products into a behavioral family model. Albeit reasonably effective, the exhaustive analysis of product lines is often infeasible due to the potentially exponential number of valid configurations. In this paper, we first present a family-based behavioral model learning techniques, called FFSMDiff. Subsequently, we report on our experience on learning family models by employing product sampling. Using 105 products of six product lines expressed in terms of Mealy machines, we evaluate the precision of family models learned from products selected from different settings of the T-wise product sampling criterion. We show that product sampling can lead to models as precise as those learned by exhaustive analysis and hence, reduce the costs for family model learning.
Year
DOI
Venue
2021
10.1007/s10664-020-09912-w
EMPIRICAL SOFTWARE ENGINEERING
Keywords
DocType
Volume
Software product lines, Model learning, Family model, T-wise sampling
Journal
26
Issue
ISSN
Citations 
1
1382-3256
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Carlos Diego Nascimento Damasceno100.34
M. R. Mousavi222011.42
Adenilso Da Silva Simão321623.24