Liblit's dissertation proposes a method for leveraging the key strength of user communities - their overwhelming numbers. His approach uses sparse random sampling rather than complete data collection for gathering information from the experiences of large numbers of software end users. It also simultaneously ensures that the observed data is an unbiased, representative subset of the complete program behavior across all runs.

The dissertation addresses several practical challenges to collecting feedback from real code, including privacy and security issues, as well as user interaction and informed consent. It presents a suite of new algorithms for statistical debugging, which involves finding and fixing software errors based on statistical analysis of sparse feedback data. The paper also reports initial results from an actual public deployment of the proposed system.

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