Learning by sampling: learning behavioral family models from software product lines

Autor(es)

Nascimento Damasceno, Carlos Diego ^[1] ; Mousavi, Mohammad Reza ^[2] ; Simao, Adenilso da Silva ^[1]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Inst Ciencias Matemat & Comp, Av Trab Sao Carlense 400, BR-13566590 Sao Carlos, SP - Brazil

^[2] Univ Leicester, Dept Informat, Univ Rd, Leicester LE1 7RH, Leics - England

• Tipo de documento: Artigo Científico
• Fonte: EMPIRICAL SOFTWARE ENGINEERING; v. 26, n. 1 JAN 8 2021.
• Citações Web of Science: 0

Resumo

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. (AU)

• Processo FAPESP: 19/06937-0 - Estudo e desenvolvimento de técnicas de teste de software e suas aplicações
• Beneficiário: Márcio Eduardo Delamaro
• Modalidade de apoio: Auxílio à Pesquisa - Regular