  Model-based testing (MBT) is a method that supports the design and execution
of test cases by models that specify the intended behaviors of a system under
test. While systematic literature reviews on MBT in general exist, the state of
the art on modeling and testing performance requirements has seen much less
attention. Therefore, we conducted a systematic mapping study on model-based
performance testing. Then, we studied natural language software requirements
specifications in order to understand which and how performance requirements
are typically specified. Since none of the identified MBT techniques supported
a major benefit of modeling, namely identifying faults in requirements
specifications, we developed the Performance Requirements verificatiOn and Test
EnvironmentS generaTion approach (PRO-TEST). Finally, we evaluated PRO-TEST on
149 requirements specifications. We found and analyzed 57 primary studies from
the systematic mapping study and extracted 50 performance requirements models.
However, those models don't achieve the goals of MBT, which are validating
requirements, ensuring their testability, and generating the minimum required
test cases. We analyzed 77 Software Requirements Specification (SRS) documents,
extracted 149 performance requirements from those SRS, and illustrate that with
PRO-TEST we can model performance requirements, find issues in those
requirements and detect missing ones. We detected three not-quantifiable
requirements, 43 not-quantified requirements, and 180 underspecified parameters
in the 149 modeled performance requirements. Furthermore, we generated 96 test
environments from those models. By modeling performance requirements with
PRO-TEST, we can identify issues in the requirements related to their
ambiguity, measurability, and completeness. Additionally, it allows to generate
parameters for test environments.
