Achieving Feature Location in Families of Models Through the Use of Search-Based Software Engineering
研究了五种搜索算法结合潜在语义分析在工业产品模型族中定位特征的效果，通过两个工业案例比较了精确率和召回率，发现进化算法与潜在语义分析的组合效果最佳。
The application of search-based software engineering techniques to new problems is increasing. Feature location is one of the most important and common activities performed by developers during software maintenance and evolution. Features must be located across families of products and the software artifacts that realize each feature must be identified. However, when dealing with industrial software artifacts, the search space can be huge. We propose and compare five search algorithms to locate features over families of product models guided by latent semantic analysis (LSA), a technique that measures similarities between textual queries. The algorithms are applied to two case studies from our industrial partners (leading manufacturers of home appliances and rolling stock) and are compared in terms of precision and recall. Statistical analysis of the results is performed to provide evidence of the significance of the results. The combination of an evolutionary algorithm with LSA can be used to locate features in families of models from industrial scenarios such as the ones from our industrial partners.