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Learning by sampling: learning behavioral family models from software product lines
Empirical Software Engineering ( IF 4.1 ) Pub Date : 2021-01-01 , DOI: 10.1007/s10664-020-09912-w
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 F F S M D i f f . 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.

中文翻译:

抽样学习:从软件产品线中学习行为家庭模型

基于家族的行为分析在单个规范工件上运行,称为家族模型,用特征约束注释以表达条件状态和转换方面的行为可变性。基于家族的行为建模为高效的基于模型的软件产品线分析铺平了道路。基于家族的行为模型学习结合特征模型分析和模型学习原理,将产品模型高效统一为家族模型,将各种产品的行为融合为行为家族模型。尽管相当有效,但由于有效配置的潜在指数数量,对产品线的详尽分析通常是不可行的。在本文中,我们首先提出了一种基于家庭的行为模型学习技术,称为 FFSMD iff。随后,我们报告了我们通过采用产品抽样来学习家庭模型的经验。使用以 Mealy 机器表示的 6 个产品线的 105 个产品,我们评估从 T-wise 产品抽样标准的不同设置中选择的产品中学习到的系列模型的精度。我们表明,产品抽样可以产生与通过详尽分析学到的模型一样精确的模型,从而降低家庭模型学习的成本。
更新日期:2021-01-01
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