Feature location is a fundamental step in software evolution tasks such as debugging, understanding, and reuse. Numerous automated and semi-automated feature location techniques (FLTs) have been proposed, but the question remains: How do we objectively determine which FLT is most effective? Existing evaluations frequently use bug fix data, which includes the location of the fix, but not what other code needs to be understood to make the fix. Existing evaluation measures such as precision, recall, effectiveness, mean average precision (MAP), and mean reciprocal rank (MRR) will not differentiate between a FLT that ranks higher these related elements over completely irrelevant ones. We propose an alternative measure of relevance based on the likelihood of a developer finding the bug fix locations from a ranked list of results. Our initial evaluation shows that by modeling user behavior, our proposed evaluation methodology can compare and evaluate FLTs fairly.