This work presents how a new approach, based on Machine Learning, to analyze any available information on the performance of COTS could help to carry out this assessment. It evaluates how valuable information might be extracted from archival data despite the limitation of the size of the analyzed dataset in a few practical examples of Total Ionizing Dose (TID) and Displacement Damage (DD) tests.