Search-based model-driven engineering is the application of search-based techniques to specific problems that are related to software engineering that is driven using software models. In this work, we make use of measures from the literature to report feature location problems in models (size and volume of the model and density, multiplicity, and dispersion of the feature being located) and a set of search strategies (random search, iterated local search, hill climbing, an evolutionary algorithm, and a hybrid between an evolutionary algorithm and hill climbing). The goal is to analyze of the impact of different values that are used to describe the feature location problems and the performance obtained by the different search strategies. We apply the search strategies to 1895 feature location problems that are obtained from 40 industrial software product lines. This work shows that: 1) the best results overall are obtained by a hybrid between evolutionary algorithm and hill climbing; 2) the size of the search space has the greatest impact on the results obtained by the search strategies; and 3) the impact of each of the measures is not the same in the five search strategies. This work highlights the use of the search strategy that produces the best results. In addition, we provide recommendations on when to use each search strategy.

基于搜索的模型驱动工程是将基于搜索的技术应用于与使用软件模型驱动的软件工程相关的特定问题。在这项工作中，我们利用文献中的措施来报告模型中的特征定位问题（模型的大小和体积以及被定位特征的密度、多样性和分散性）和一组搜索策略（随机搜索、迭代局部搜索、爬山、进化算法以及进化算法和爬山的混合）。目标是分析用于描述特征定位问题的不同值的影响以及通过不同搜索策略获得的性能。我们将搜索策略应用于从 40 个工业软件产品线中获得的 1895 个特征定位问题。这项工作表明：1）总体上最好的结果是通过进化算法和爬山法的混合获得的；2）搜索空间的大小对搜索策略得到的结果影响最大；3）在五种搜索策略中，每种措施的影响并不相同。这项工作突出了产生最佳结果的搜索策略的使​​用。此外，我们还提供有关何时使用每种搜索策略的建议。3）在五种搜索策略中，每种措施的影响并不相同。这项工作突出了产生最佳结果的搜索策略的使​​用。此外，我们还提供有关何时使用每种搜索策略的建议。3）在五种搜索策略中，每种措施的影响并不相同。这项工作突出了产生最佳结果的搜索策略的使​​用。此外，我们还提供有关何时使用每种搜索策略的建议。