Characterizing the Effects of Learning When Reverse Engineering Multiple Samples of the Same Product

Abstract

Reverse engineering is the process of extracting information about a product from the product itself. An estimate of the barrier and time to extract information from any product is useful for the original designer and those reverse engineering, as both are affected by reverse engineering activities. The authors have previously presented a set of metrics and parameters to estimate the barrier and time to reverse engineer a product once. This work has laid the foundation for the developments of the current paper, which address the issue of characterizing the reverse engineering time and barrier when multiple samples of the same product are reverse engineered. Frequently in practice, several samples of the same product are reverse engineered to increase accuracy, extract tolerances, or to gather additional information from the product. In this paper, we introduce metrics that (i) characterize learning in the reverse engineering process as additional product samples are evaluated and (ii) estimate the total time to reverse engineer multiple samples of the same product. Additionally, an example of reverse engineering parts from a control valve is introduced to illustrate how to use the newly developed metrics and to serve as empirical validation.