With the increased adoption of Model-Driven Engineering (MDE), where models are being used as the primary artifact of software, it is apparent that greater attention to the quality of the models is necessary. Traditionally, refactoring is used to enhance the quality of software systems at the source-code level; however, applying refactoring at the model level will have a more significant improvement on the system. After refactoring a model, proving that it still preserves its original behavior is crucial. In this paper, we present a process for applying refactoring transformations to the Extended Finite State Machine (EFSM) models using verified transformation rules that have been proven to preserve the model's original behavior. We provide a simplified three-step verification approach that can be used to prove that a transformation rule will generate a transformed model that is semantically equivalent to the original model. To do this, we formally define semantical equivalence at three different levels of granularity: models, sub-models, and transitions. Additionally, we introduce five model transformation rules and we demonstrate how our verification approach is used to prove the correctness of these rules. Finally, we present two case studies where we apply the proposed transformation process which adopts the five verified transformation rules. Using model testing, we show that applying a sequence of transformations using the verified transformation rules will keep both the original and the transformed model semantically equivalent. Additionally, the case studies show that model transformation can be used to enhance certain pre-defined model characteristics.

Software maintenance is becoming more challenging with the increased complexity of the software and the frequently applied changes. Performing impact analysis before the actual implementation of a change is a crucial task during system maintenance. While many tools and techniques are available to measure the impact of a change at the code level, only a few research work is done to measure the impact of a change at an earlier stage in the development process. Measuring the impact of a change at the model level speeds up the maintenance process allowing early discovery of critical components of the system before applying the actual change at the code level. In this paper, we present model-based impact analysis approach for state-based systems such as telecommunication or embedded systems. The proposed approach uses model dependencies to automatically measure the expected impact for a requested change instead of relying on the expertise of system maintainers, and it generates two impact sets representing the lower bound and the upper bound of the impact. Although it can be extended to other behavioral models, the presented approach mainly addresses extended finite-state machine (EFSM) models. An empirical study is conducted on six EFSM models to investigate the usefulness of the proposed approach. The results show that on average the size of the impact after a single modification (a change in a one EFSM transition) ranges between 14 and 38 % of the total size of the model. For a modification involving multiple transitions, the average size of the impact ranges between 30 and 64 % of the total size of the model. Additionally, we investigated the relationships (correlation) between the structure of the EFSM model, and the size of the impact sets. Upon preliminary analysis of the correlation, the concepts of model density and data density were defined, and it was found that they could be the major factors influencing the sizes of impact sets for models. As a result, these factors can be used to determine the types of models for which the proposed approach is the most appropriate.

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.