特征模型理解的眼动追踪实验研究
An Empirical Eye-Tracking Study of Feature Model Comprehension
摘要：
软件产品线（SPL）是相关软件系统的系列，其区别在于每个系统提供的一组功能。特征模型是对SPL的可变性进行建模的事实标准，因为它们描述了构成SPL的特征、特征之间的关系以及特征的所有组合。由于其关键作用，特征模型是SPL工程中许多任务的核心。我们的工作提出了一个实证研究的理解功能模型的任务，检查配置的有效性。本研究探讨了特征数量和交叉树约束数量与被试回答效度检验问题的准确性之间的关系，并利用眼注视分析了被试对注视信息的理解难度和对特征模型刺激不同部分的认知加工量。我们发现，答案的准确性不涉及单独的功能的数量或交叉树约束的功能模型的数量，但这两个因素确实显示出准确性的相互作用。此外，我们的研究确定了在固定次数和固定时间方面具有交叉树约束的特征模型的差异，但在没有交叉树约束的那些模型中没有差异。
英文摘要：
Software Product Lines (SPLs) are families of related software systems which are distinguished by the set of features each system provides. Feature Models are the de facto standard for modelling the variability of SPLs because they describe the features, their relations, and all the combinations of features that constitute a SPL. Because of their key role, feature models are at the core of many tasks in SPL engineering. Our work presents an empirical study on the comprehension of feature models for the task of checking the validity of configurations. Our study explored the relation between the number of features and the number of cross-tree constraints with the accuracy of participants' answers to validity checking questions, and used eye fixations for analyzing the difficulty in interpreting fixated information and the amount of cognitive processing of the different parts of the feature model stimuli. We found that answer accuracy does not relate individually to the number of features or to the number of cross-tree constrains of a feature model, but both factors do show an interaction on accuracy. Additionally, our study identified differences in feature models with cross-tree constraints in both number of fixations and fixation time, but no differences in those models without cross-tree constraints.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
