Automatically exploiting implicit design knowledge when solving the class responsibility assignment problem

Assigning responsibilities to classes is not only vital during initial software analysis/design phases in object-oriented analysis and design (OOAD), but also during maintenance and evolution phases, when new responsibilities have to be assigned to classes or existing responsibilities have to be changed. Class Responsibility Assignment (CRA) is one of the most complex tasks in OOAD as it heavily relies on designers' judgment and implicit design knowledge (DK) of design problems. Since CRA is highly dependent on the successful use of implicit DK, (semi)-automated approaches that help designers to assign responsibilities to classes should make implicit DK explicit and exploit the DK effectively. In this paper, we propose a learning based approach for the Class Responsibility Assignment (CRA) problem. A learning mechanism is introduced into Genetic Algorithm (GA) to extract the implicit DK about which responsibilities have a high probability to be assigned to the same class, and then the extracted DK is employed automatically to improve the design quality of the generated solutions. The proposed approach has been evaluated through an experimental study with three cases. By comparing the solutions obtained from the proposed approach and the existing approaches, the proposed approach can significantly improve the design quality of the generated solutions to the CRA problem, and the generated solutions by the proposed approach are more likely to be accepted by developers from the practical aspects.