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.

SUMMARY During regression testing, a modified system is often retested using an existing test suite. Since the size of the test suite may be very large, testers are interested in detecting faults in the modified system as early as possible during this retesting process. Test prioritization attempts to order tests for execution so that the chances of early detection of faults during retesting are increased. The existing prioritization methods are based on the source code of the system under test. In this paper, we present and evaluate two model‐based selective methods and a dependence‐based method of test prioritization utilizing the state‐based model of the system under test. These methods assume that the modifications are made both on the system under test and its model. The existing test suite is executed on the system model and information about this execution is used to prioritize tests. Execution of the model is inexpensive as compared with execution of the system under test; therefore, the overhead associated with test prioritization is relatively small. In addition, we present an analytical framework for evaluation of test prioritization methods. This framework may reduce the cost of evaluation as compared with the framework that is based on observation. We have performed an empirical study in which we compared different test prioritization methods. The results of the empirical study suggest that system models may improve the effectiveness of test prioritization with respect to early fault detection. Copyright © 2011 John Wiley & Sons, Ltd.

During regression testing, a modified system needs to be retested using the existing test suite. Since test suites may be very large, developers are interested in detecting faults in the system as early as possible. Test prioritization orders test cases for execution to increase potentially the chances of early fault detection during retesting. Most of the existing test prioritization methods are based on the code of the system, but model-based test prioritization has been recently proposed. System modeling is a widely used technique to model state-based systems. The existing model based test prioritization methods can only be used when models are modified during system maintenance. In this paper, we present model-based prioritization for a class of modifications for which models are not modified (only the source code is modified). After identification of elements of the model related to source-code modifications, information collected during execution of a model is used to prioritize tests for execution. In this paper, we discuss several model-based test prioritization heuristics. The major motivation to develop these heuristics was simplicity and effectiveness in early fault detection. We have conducted an experimental study in which we compared model-based test prioritization heuristics. The results of the study suggest that system models may improve the effectiveness of test prioritization with respect to early fault detection.

During regression testing, a modified system is retested using the existing test suite. Because the size of the test suite may be very large, testers are interested in detecting faults in the system as early as possible during the retesting process. Test prioritization tries to order test cases for execution so the chances of early detection of faults during retesting are increased. The existing prioritization methods are based on the code of the system. System modeling is a widely used technique to model state-based systems. In this paper, we present methods of test prioritization based on state-based models after changes to the model and the system. The model is executed for the test suite and information about model execution is used to prioritize tests. Execution of the model is inexpensive as compared to execution of the system; therefore the overhead associated with test prioritization is relatively small. In addition, we present an analytical framework for evaluation of test prioritization methods. This framework may reduce the cost of evaluation as compared to the existing evaluation framework that is based on experimentation (observation). We have performed an experimental study in which we compared different test prioritization methods. The results of the experimental study suggest that system models may improve the effectiveness of test prioritization with respect to early fault detection.

System modeling is a widely used technique to model state-based systems. Several state-based languages are used to model such systems, e.g., EFSM (extended finite state machine), SDL (specification description language) and state charts. Although state-based modeling is very useful, system models are frequently large and complex and are hard to understand and modify. Slicing is a well-known reduction technique. Most of the research on slicing is code-based. There has been limited research on specification-based slicing and model-based slicing. In this paper, we present an approach to slicing state-based models, in particular EFSM models. Our approach automatically identifies the parts of the model that affect an element of interest using EFSM dependence analysis. Slice reduction techniques are then used to reduce the size of the EFSM slice. Our experience with the presented slicing approach showed that significant reduction of state-based models could be achieved.

Model based testing is a system testing technique used to test software systems modeled by formal description languages, e.g., an extended finite state machine (EFSM). System models are frequently changed because of specification changes. Selective test generation techniques are used to test the modified parts of the model. However, the size of regression test suites still may be very large. In this paper, we present a model-based regression testing approach that uses EFSM model dependence analysis to reduce regression test suites. The approach automatically identifies the difference between the original model and the modified model as a set of elementary model modifications. For each elementary modification, regression test reduction strategies are used to reduce the regression test suite based on EFSM dependence analysis. Our initial experience shows that the approach may significantly reduce the size of regression test suites.

Testing large software systems is very laborious and expensive. Model-based test generation techniques are used to automatically generate tests for large software systems. However, these techniques require manually created system models that are used for test generation. In addition, generated test cases are not associated with individual requirements. In this paper, we present a novel approach of requirement-based test generation. The approach accepts a software specification as a set of individual requirements expressed in textual and SDL formats (a common practice in the industry). From these requirements, system model is automatically created with requirement information mapped to the model. The system model is used to automatically generate test cases related to individual requirements. Several test generation strategies are presented. The approach is extended to requirement-based regression test generation related to changes on the requirement level. Our initial experience shows that this approach may provide significant benefits in terms of reduction in number of test cases and increase in quality of a test suite.