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Learning by Sampling: Learning Behavioral Family Models from Software Product Lines

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posted on 2020-11-27, 10:29 authored by Carlos Diego Nascimento Damasceno, Mohammad reza Mousavi, Adenilso da Silva Simao
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.

History

Citation

Empir Software Eng 26, 4 (2021). https://doi.org/10.1007/s10664-020-09912-w

Author affiliation

Department of Engineering

Version

  • AM (Accepted Manuscript)

Published in

Empirical Software Engineering: an international journal

Volume

26

Issue

4

Publisher

Springer Verlag

issn

1382-3256

Acceptance date

2020-10-23

Copyright date

2021

Available date

2022-01-08

Language

en

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