Context: A Software Product Line (SPL) can express the variability of a system through the specification of configuration options. Evaluating performance characteristics, such as the system response time and resource utilization, of a software product is challenging, even more so in the presence of uncertain values of the attributes.

Objective: The goal of this paper is to automate the generation of performance models for software products derived from the feature model by selection heuristics. We aim at obtaining model-based predictive results to quantify the correlation between the features, along with their uncertainties, and the system performance. This way, software engineers can be informed on the performance characteristics before implementing the system.

Method: We propose a tool-supported framework that, starting from a feature model annotated with performance-related characteristics, derives Queueing Network (QN) performance models for all the products of the SPL. Model-based performance analysis is carried out on the models obtained by selecting the products that show the maximum and minimum performance-based costs.

Results: We applied our approach to almost seven thousand feature models including more than one hundred and seventy features. The generation of QN models is automatically performed in much less than one second, whereas their model-based performance analysis embeds simulation delays and requires about six minutes on average.

Conclusion: The experimental results confirm that our approach can be effective on a variety of systems for which software engineers may be provided with early insights on the system performance in reasonably short times. Software engineers are supported in the task of understanding the performance bounds that may encounter when (de)selecting different configuration options, along with their uncertainties.

背景信息：软件产品线（SPL）可以通过指定配置选项来表达系统的可变性。评估软件产品的性能特征（例如系统响应时间和资源利用率）具有挑战性，尤其是在属性值不确定的情况下。

目的：本文的目的是通过选择启发式方法自动生成从功能模型派生的软件产品性能模型。我们旨在获得基于模型的预测结果，以量化特征之间的相关性，不确定性以及系统性能。这样，可以在实施系统之前通知软件工程师性能特征。

方法：我们提出了一个工具支持的框架，该框架从带有性能相关特征的特征模型开始，为SPL的所有产品推导排队网络（QN）性能模型。基于模型的性能分析是对通过选择显示基于性能的最大和最小成本的产品而获得的模型进行的。

结果：我们将我们的方法应用于近七千个要素模型，其中包括一百七十多个要素。QN模型的生成会在不到一秒钟的时间内自动完成，而基于模型的性能分析却嵌入了仿真延迟，平均需要大约六分钟的时间。

结论：实验结果证实，我们的方法可以在多种系统上有效，对于这些系统，软件工程师可以在相当短的时间内就系统性能获得早期见解。支持软件工程师的任务是了解（取消）选择不同的配置选项时可能遇到的性能界限及其不确定性。