Software reliability is a primary concern in the construction of software, and thus a fundamental component in the definition of software quality. Analyzing software reliability requires a \emph{specification} of the intended behavior of the software under analysis. Unfortunately, software many times lacks such specifications. This issue seriously diminishes the analyzability of software with respect to its reliability. Thus, finding novel techniques to capture the intended software behavior in the form of specifications would allow us to exploit them for automated reliability analysis.

Our research focuses on the application of learning techniques to automatically distinguish correct from incorrect software behavior. The aim here is to decrease the developer’s effort in specifying oracles, and instead \emph{generating} them from actual software behaviors.