Context Model-Based Testing (MBT) can be executed in either offline or online mode. While offline MBT pre-generates test cases that can be independently executed and resumed, online MBT dynamically interacts with the system under test by generating test steps on-the-fly based on runtime feedback. This adaptive exploration makes online MBT particularly attractive in industrial contexts; however, it also introduces a structural fragility: when an unexpected failure occurs during execution, the testing session often terminates prematurely, preventing further exploration of the model. Objective The objective of this study is to improve the robustness and usability of online MBT by enabling execution to continue in the presence of localized assertion failures without masking genuine faults. Method We propose two lightweight execution-level fault-tolerance heuristics: Retry Failed Node and Blacklist Failed Node. The heuristics were implemented in the GraphWalker MBT tool by extending its execution and exception-handling mechanisms and evaluated using a large-scale industrial MBT suite at Testinium. Results The retry heuristic mitigates transient and flaky failures, while the blacklist heuristic enables continued traversal after persistent localized failures. Together, they significantly reduce premature termination of online MBT execution with minimal and bounded overhead. Conclusion Fault-tolerant execution semantics are essential for making online MBT practical in real-world testing. The proposed heuristics improve execution continuity and support broader industrial adoption of online MBT.

Context Model-Based Testing (MBT) was first introduced in 1970′s, and has the potential to improve efficiency and effectiveness of testing. However, its adoption—especially for web applications—has been hindered by the effort required to manually design MBT models, and keep them updated. Objective Based on the above challenge in a real industrial context, this study introduces an automated approach to reduce that effort by reverse engineering MBT models from clickstream data captured during users' interaction with web applications. Method We have developed and present in this paper an open-source tool, named MBTModelGenerator , which logs user interactions via a lightweight JavaScript module in the front-end, and transmits them to a REST API backend. These interactions are then transformed into directly executable MBT models in the input format of an open-source MBT tool named GraphWalker . Results The tool was evaluated on two representative open-source web applications, Spring PetClinic and a Task Manager web app, and is under evaluation in several large-scale industrial testing projects. The generated MBT models accurately reflected user navigation flows and could be executed in the GraphWalker MBT tool without any manual changes. Using the tool has significantly reduced the effort of MBT model design by more than 90%, while still allowing test engineers to inspect and refine the generated models for completeness. Conclusion Our approach facilitates lightweight adoption of MBT by automating model generation, which is the most effort intensive phase of MBT. To ensure correctness and completeness, the generated models should still be reviewed by test engineers —but that effort remains substantially lower than designing MBT models from scratch. The tool is in active industrial use and available as open-source for reuse and further development.