model driven software engineering

Model-Driven Software Engineering (MDSE) is a software development methodology that reduces the cost and production time of the final product by concentratingon a higher level of abstraction. The main focus of MDSE is to generate automated code by applying different types of transformations to high-level models. The effectiveness of MDSE has been proved in different domains for solving various types of problems.This research provides the bibliometric analysis of research in the field of MDSE. The population of the study consists of all MDSE articles indexed in the Web of Science database in ten years from 2010 to 2019. The work aims to identify the volume of scientific production, the most influential countries, universities, authors, and journals, the cooperation network among the countries, universities, and authors, keyword ranking, and Co-word analysis of keywords and titles of the articles. Keyword analysis revealed that Model Transformation and Model Checking are two very important clusters and topics of interest to researchers in this field. The results provide valuable insights that can be used as a guideline by both fresh and experienced researchers for the current state and future trend of MDSE research in different scientific disciplines to establish a baseline beforeinitiating an MDSE research project in the future.

The Class Responsibility Assignment (CRA) problem is one of the most important problems in Object-Oriented Software Engineering. It is a Search-based optimization problem to assign attributes and methods to a set of classes such that the related class diagram has maximum cohesion and minimum coupling. Due to the large and complex search space of the problem, finding an optimal solution is a costly and challenging task. In this regard, the use of optimization approaches can be promising. In this paper, the Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) algorithms are implemented using Model-Driven Engineering (MDE) techniques for solving the CRA problem. To evaluate the proposed approach, the effectiveness of provided algorithms is presented using models with different scales. Additionally, the results are compared with existing solutions for the CRA problem in the community. The results indicated that for large-scale models the ACO algorithm could find a much better solution in less time compared to the PSO algorithm.