Automated Assistance for Eliciting User Expectations

People often use software for mundane tasks and expect it to be dependable enough for their needs. Unfortunately, the incomplete and imprecise specifications of such everyday software inhibit many dependability enhancement techniques because these require a model of proper behavior for failure detection. We offer a user-centered approach for creating a model of proper behavior. This approach is based on satisfying the user expectations - software behavior the user relies on - rather than demanding perfect specifications. It utilizes data mining through a novel template mechanism, to help users make their expectations precise. The resulting precise expectations can then serve as proxies for missing specifications in detecting unexpected data behavior. We concentrate on data feeds; continuous streams of data, a challenging example of everyday software. Using our method on a real world data feed, it took just hours to detect problems that had taken the data providers months to detect independently. These problems surprised even our user - a domain expert that had previously analyzed the same data feed. Systematic analysis further supports the usefulness of our method.