Towards Minimizing the Impact of Changes Using Search-Based Approach

Software maintenance is becoming more challenging with the increased complexity of the software and the frequently applied modifications. To manage this complexity, systems development is headed towards Model-driven engineering (MDE) and search-based software engineering (SBSE). Additionally, prior to applying a change to these complex systems, change impact analysis is usually performed in order to determine the scope of the change, its feasibility, and the time and resources required to implement the change. The bigger the scope, the riskier the change is on the system. In this paper, we introduce a set of transformation rules for Extended Finite State Machine (EFSM) models of state-based systems. These transformation rules can be used as the basis for search-based model optimization in order to reduce the average impact of a potential change applied to an EFSM model. Assuming that Model-driven development is adopted for the implementation of a state-based system, reducing the change impact at the model level will lead to reducing the impact at the system level. An exploratory study is performed to measure the impact reduction for a given EFSM model when the transformation rules are applied by a search-based algorithm. The initial results show a promising usage of the transformation rules which can lead to a reduction of more than 50% of the initial average change impact of the model.