Genetic Synthesis of Software Architecture

Abstract

This thesis experiments with a novel approach to applying genetic algorithms in software architecture design by giving the structure of an architecture at a highly abstract level.Previously in the literature, genetic algorithms are used only to improve existing architectures. The structure and evaluation of software architectures and the principles of meta-heuristic search algorithms are introduced to give a basis to understand the implementation. Current research in the field of search-based software engineering is explored to give a perspective to the implementation presented in this thesis. The chosen genetic construction of software architectures is based on a model which contains information of a set of responsibilities and dependencies between them. An implementation using this model is presented, as well as test results achieved from a case study made on a sketch of an electronic home control system. The test results show that quality results can be achieved using the selected approach and that the presented implementation is a good starting point for future research.

Synthesizing Architecture from Requirements: A Genetic Approach

“…The chromosome handled by the genetic algorithm is gained by collecting the supergenes, i.e., all data regarding all operations, thus representing a whole view of the architecture. A more detailed specification of the architecture representation is given by Räihä et al (2008a;2008b).…”

Using simulated annealing for producing software architectures

“…Räihä et al [14] have taken a more advanced approach to designing software architecture than Simons and Parmee [17] by starting the design from a responsibility dependency graph and developing the architecture further than the class distribution of actions and data. A GA is used for the automation of design.…”

“…Notice that the test runs depicted in Figures 2-4 use the same fitness function as the genetic algorithm used in the rest of the paper (and the same used in our previous tests [13,14]). …”

“…Genetic algorithms (GAs) are shown to be a feasible method for producing software architectures from functional requirements [14,15]. It is then natural to ask if other search methods are capable of producing equally good architectures alone or in cooperation with genetic algorithms.…”

Algorithms of the UML Class Diagram Analysis and Their Effectiveness: Strategy and Interface Insertion Transformations

“…An algorithm receives a UML diagram and a fitness function as an input and gives a result diagram as an output [5][6][7][8][9][10].…”