2023-12-142023-12-142020978-989-54659-0-3978-1-7281-6724-4https://hdl.handle.net/10669/90667Model-based testing (MBT) is an approach for auto- matically generating test cases from a model of the system under test. Existing MBT tools support the automation of this process at varying degrees. One such tool is MBT4J, a research platform that extends ModelJUnit, offering a high level of automation. We ex- tended MBT4J with two graph-based algorithms: the Chinese Postman Problem (CPP) and Breadth-First Search (BFS). The purpose of this study is to evaluate the efficacy of these two new algorithms added to MBT4J by comparing them to previous algo- rithms implemented in the platform. A case study was conducted using two open-source Java applications from public repositories, and twenty-one different configurations. The CPP tester per- formed similarly to previous testers in terms of time and coverage, and in addition, it resulted in a greater percentage of failed test cases in one application. The BFS tester was able to generate a greater amount of test cases when using fewer resources. We thus recommend using these algorithms for generating test cases for systems with complex models.engacceso abiertoALGORITHMSCASE STUDIESCOMPUTER LANGUAGESCOMPUTER APPLICATIONScomunicación de congreso10.23919/CISTI49556.2020.9141107