Efficient Management of Large Models via Static Analysis

As the size of software and system models grows, scalability issues in the current generation of model management languages (e.g. transformation, validation) and their supporting tooling become more prominent. With the growing popularity of MDE in larger projects, the efficient management and processing of large models have become critical considerations. To address this challenge, execution engines of model management programs need to become more efficient in their use of system resources. Effective resource management is essential not only for minimizing execution costs but also for optimizing resource usage, particularly in scenarios where resources are billed based on usage patterns. This thesis addresses this challenge by presenting an approach to enhance the efficiency of model management programs, which play a pivotal role in querying and manipulating models. This approach focuses on enabling execution engines to load only the necessary parts of models, minimizing the overhead associated with loading unnecessary model elements into memory. Through the utilization of in-advance knowledge obtained from static analysis of model management programs, execution engines can identify, load, and process only the model elements essential for execution. Furthermore, the approach ensures that elements are disposed of from memory when no longer needed, optimizing both memory utilization and processing time. Experimental evaluations demonstrate that our approach empowers model management programs to process larger models faster with a reduced memory footprint compared to current state-of-the-art approaches.