Encoding Feature Models in Neo4j Graph Database
Hazim Shatnawi, Jamil M. Saquer · 2024
This study introduces an innovative approach to encoding and analyzing feature models within the Network Exploration and Optimization for Java (Neo4j) graph database, significantly enhancing the management of complex Software Product Lines (SPLs). We present a comparative analysis of traditional loading techniques against Neo4j's batch importer and the Awesome Procedures on Cypher (APOC) library, demonstrating the superior efficiency and effectiveness of our proposed methods, especially in handling large datasets. Our methodology extends beyond mere encoding; it capitalizes on Neo4j's Graph Data Science (GDS) library, employing Depth-First Search (DFS) and other advanced traversal techniques to navigate and manipulate these complex structures. The findings reveal not only a significant enhancement in the processing and analysis of feature models but also underscore the potential for more sophisticated SPL management strategies. By integrating innovative loading techniques, encoding strategies, and GDS traversal methods, this study lays a robust foundation for future advancements in the field.