Software Product Lines (SPLs) make it possible to configure a single system based on features in order to create many different variants and cater to a wide range of customers with varying requirements. This configuration space is often modeled using Feature Models (FMs). However, in practice, the SPL (and consequently the FM) is often created after a set of variants has already been created manually. Automating the task of reverse engineering a feature model that describes a set of variants makes the process of adopting an SPL easier. The genetic programming pipeline is a good fit for feature models and has been shown to produce good reverse engineering results. In this paper, we replicate the results of such an existing approach with a larger set of feature models and investigate the effects of various genetic programming parameters and operators on the results. The design of our replication experiments employs three perspectives: duplicate the exact conditions using various features models, study the interaction of two parameters of the genetic programming approach, and optimize the values for the population and generation parameters and for the mutation and crossover operators. Results reinforce the previously obtained outcome, the original study being confirmed. The relations between the number of features and number of generations, respectively number of features and size of populations were also investigated and best values based on obtained results are provided. The current study also aimed to optimize various parameters of the genetic programming approach, the interpretation of those experiments discovering concrete values.

软件产品线（SPL）使基于功能配置单个系统成为可能，以便创建许多不同的变体并满足具有不同需求的广泛客户。此配置空间通常使用功能模型（FM）进行建模。但是，实际上，通常会在手动创建一组变体之后创建SPL（并因此创建FM）。对描述一组变体的特征模型进行逆向工程自动化可以使采用SPL的过程变得更加容易。遗传程序设计管道非常适合特征模型，并且已显示出良好的逆向工程结果。在本文中，我们用一大套特征模型复制了这种现有方法的结果，并研究了各种遗传编程参数和算子对结果的影响。我们的复制实验设计从三个角度出发：使用各种特征模型复制精确条件，研究遗传编程方法的两个参数之间的相互作用，以及优化种群和世代参数以及突变和交叉算子的值。结果加强了先前获得的结果，证实了原始研究。还研究了特征数量与世代数量之间的关系，分别研究了特征数量与种群数量之间的关系，并根据获得的结果提供了最佳值。