Several experience reports illustrate that mutation testing is capable of supporting a “shift-left” testing strategy for software systems coded in textual programming languages like C++. For graphical modelling languages like Simulink, such experience reports are missing, primarily because of a lack of adequate tool support. In this paper, we present a proof-of-concept (named MUT4S LX) for automatic mutant generation and test execution of Simulink models. MUT14SLX features 15 mutation operators which are modelled after realistic faults (mined from an industrial bug database) and are fast to inject (because we only replace parameter values within blocks). An experimental evaluation on a sample project (a Helicopter Control System) demonstrates that MUT4SLX is capable of injecting 70 mutants in less than a second, resulting in a total analysis time of 8.14 hours.

Model-based GUI testing has achieved widespread recognition in academy thanks to its advantages compared to code-based testing due to its potentials to automate testing and the ability to cover bigger parts more efficiently.In this study design paper, we address the scalability part of the model-based GUI testing by using community detection algorithms.A case study is presented as an example of possible improvements to make a model-based testing approach more efficient.We demonstrate layered ESG models as an example of our approach to consider the scalability problem.We present rough calculations with expected results, which show 9 times smaller time and space units for 100 events in the ESG model when a community detection algorithm is applied.