The fundamental idea of the research described in this paper is to define an innovative experimental metric which operates on a series of structural parameters of programs: applying linear programming techniques on these parameters it is possible to define a measurement which can predict the risk level of a program, namely how prone it is to containing faults.

The new proposed model represents the software modules as points in a -dimensional space (every dimension is one of the structural attributes for each module). Starting from this model the problem to find out the more dangerous files is brought-back to the problem to separate two sets in Rⁿ . The classification procedure is divided in two steps: the learning phase, which is used to tune the model on the specified environment and the effective selection, which is the real measure. Our engine was built using the MSM-T method (Multisurface Method Tree), a greedy algorithm, which iterative divides the space in polyhedral regions till it reaches a void set. It is so possible to divide the n-dimensional space and find out the risk-regions of the space which represent the dangerous modules.

All the process was tested in an industrial application, to validate experimentally the soundness of the methodology.