loading page

On the Interaction between the Search Parameters and the Nature of the Search Problems in Search-Based Model-Driven Engineering
  • +1
  • Isis Roca,
  • Jaime Font,
  • Lorena Arcega,
  • Carlos Cetina
Isis Roca
Universidad San Jorge

Corresponding Author:[email protected]

Author Profile
Jaime Font
Universidad San Jorge
Author Profile
Lorena Arcega
Universidad San Jorge
Author Profile
Carlos Cetina
Universidad San Jorge
Author Profile


The use of Search-Based software engineering to address Model-Driven Engineering activities (SBMDE) is becoming more popular. Many maintenance tasks can be reformulated as a search problem, and, when those tasks are applied to software models, the search strategy has to retrieve a model fragment. There are no studies on the influence of the search parameters when applied to software models. This paper evaluates the impact of different search parameter values on the performance of an evolutionary algorithm whose population is in the form of software models. Our study takes into account the nature of the model fragment location problems (MFLPs) in which the evolutionary algorithm is applied. The evaluation searches 1,895 MFLPs (characterized through five measures that define MFLPs) from two industrial case studies and uses 625 different combinations of search parameter values. The results show that the impact on the performance when varying the population size, the replacement percentage, or the crossover rate produces changes of around 30% in performance. With regard to the nature of the problems, the size of the search space has the largest impact. Search parameter values and the nature of the MFLPs influence the performance when applying an evolutionary algorithm to perform fragment location on models. Search parameter values have a greater effect on precision values, and the nature of the model fragment location problems has a greater effect on recall values. Our results should raise awareness of the relevance of the search parameters and the nature of the problems for the SBMDE community.
24 Jan 2023Submitted to Software: Practice and Experience
24 Jan 2023Submission Checks Completed
24 Jan 2023Assigned to Editor
03 Feb 2023Review(s) Completed, Editorial Evaluation Pending
05 Feb 2023Reviewer(s) Assigned
17 Sep 2023Editorial Decision: Revise Major
29 Jan 2024Review(s) Completed, Editorial Evaluation Pending
29 Jan 2024Editorial Decision: Accept