1. A computer-implemented method comprising: collecting, via a hardware processor, using a domain metadescriptor in a specific context triggered by a matrix execution, first data associated with an initial behavior phase of a service defining a first set of values; collecting, via the hardware processor, using the domain metadescriptor in the specific context triggered by the matrix execution, second data associated with an exercised behavior phase of the service defining a second set of values; clustering the first and second sets of values into respective clusters using a machine learning hierarchical clustering algorithm; and computing, via the hardware processor, a deviation between the first data associated with the initial behavior phase and second data associated with the exercised behavior phase of the service based on the respective clusters.