Abstract
Achieving and maintaining high software quality is most dependent on how easily the software engineer least familiar with the system can understand the system's code. Understanding attributes of cognitive processes can lead to new software metrics that allow the prediction of human performance in software development and for assessing and improving the understandability of text and code. In this research we present novel metrics based on current understanding of short-term memory performance, to predict the location of high frequencies of errors and to evaluate the quality of a software system. We further enhance these metrics by applying static and dynamic program slicing to provide programmers with additional guidance during software inspection and maintenance efforts.