The user-acquainted feature collects user-specific data regarding the types of advice that have been sought over time and uses this historical information to update the probabilities to affect the firing of its rules and the ordering of the recommendations of the expert system. These updates are done on auser-specific basis so that the expert can more closely emulate a true expert by providing more informed advice. One example of a place where evidence suggests that such a feature would be useful is in the area of debugging of computer programs, especially in support of novice programmers who tend, as individuals, to commit similar classes of errors over time, but who, as a group, commit very different types of errors. We conjecture that the user-acquainted feature, which can keep track of the tendencies of the users and take them into account in the evaluation of diagnostics, will be more effectiveand efficient in determining the fault. In this paper, we discuss the statistical analyses necessary to implement this feature in an expert system for debugging errors in SAS.