One of the challenging issues in improving the test efficiency isthat of achieving a balance between testing goals and testing resources.Test execution scheduling is one way of saving time andbudget, where a set of test cases are grouped and tested at thesame time. To have an optimal test execution schedule, all relatedinformation of a test case (e.g. execution time, functionality to betested, dependency and similarity with other test cases) need tobe analyzed. Test scheduling problem becomes more complicatedat high-level testing, such as integration testing and especially inmanual testing procedure. Test specifications are generally writtenin natural text by humans and usually contain ambiguity anduncertainty. Therefore, analyzing a test specification demands astrong learning algorithm. In this position paper, we propose anatural language processing-based approach that, given test specificationsat the integration level, allows automatic detection oftest cases semantic dependencies. The proposed approach utilizesthe Doc2Vec algorithm and converts each test case into a vectorin n-dimensional space. These vectors are then grouped using theHDBSCAN clustering algorithm into semantic clusters. Finally, aset of cluster-based test scheduling strategies are proposed for execution.The proposed approach has been applied in a sub-systemfrom the railway domain by analyzing an ongoing testing projectat Bombardier Transportation AB, Sweden.