Program risk definition via linear programming techniques

The paper defines an innovative experimental metric which operates on a series of structural parameters of programs: by applying linear programming techniques on these parameters it is possible to define a measurement which can predict the risk level of a program. 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. 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 iteratively divides the space in polyhedral regions till it reaches a void set. It is thus 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.