Resumen de Definition of descriptive and diagnostic measurements for model fragment retrieval
Manuel Ballarin Naya
Nowadays, software exists in almost everything. Companies often develop and maintain a collection of custom-tailored software systems that share some common features but also support customer-specific ones. As the number of features and the number of product variants grows, software maintenance is becoming more and more complex. To keep pace with this situation, Model-Based Software Engineering Community is addressing a key-activity: Model Fragment Location (MFL). MFL aims at identifying model elements that are relevant to a requirement, feature, or bug. Many MFL approaches have been introduced in the last few years to address the identification of the model elements that correspond to a specific functionality. However, there is a lack of detail when the measurements about the search space (models) and the measurements about the solution to be found (model fragment) are reported. The goal of this thesis is to provide insights to MFL Research Community of how to improve the report of location problems. We propose using five measurements (size, volume, density, multiplicity, and dispersion) to report the location problems during MFL. The usage of these novel measurements support researchers during the creation of new MFL approaches and during the improvement of those existing ones. Using two different case studies, both real and industrial, we emphasize the importance of these measurements in order to compare results in a deeply way. The results of the research have been redacted and published in forums, conferences, and journals specialized in the topics and context of the research.
This thesis is presented as compendium of articles according the regulations in Universitat Politècnica de València. This thesis document introduces the topics, context, and objectives of the research, presents the academic publications that have been published as a result of the work, and then discusses the outcomes of the investigation.
