An investigation on the use of machine learned models for estimating correction costs

We present the results of an empirical study in which we have investigated machine learning (ML) algorithms with regard to their capabilities to accurately assess the correctability of faulty software components. Three different families of algorithms have been analyzed. We have used (1) fault data collected on corrective maintenance activities for the Generalized Support Software reuse asset library located at the Flight Dynamics Division of NASA's GSFC and (2) product measures extracted directly from the faulty components of this library.