A Negative Input Space Complexity Metric as Selection Criterion for Fuzz Testing

Abstract

Fuzz testing is an established technique in order to find zero-day-vulnerabilities by stimulating a system under test with invalid or unexpected input data. However, fuzzing techniques still generate far more test cases than can be executed. Therefore, different kinds of risk-based testing approaches are used for test case identification, selection and prioritization. In contrast to many approaches that require manual risk analysis, such as fault tree analysis, failure mode and effect analysis, and the CORAS method, we propose an automated approach that takes advantage of an already shown correlation between interface complexity and error proneness. Since fuzzing is a negative testing approach, we propose a complexity metric for the negative input space that measures the boundaries of the negative input space of primitive types and complex data types. Based on this metric, the assumed most error prone interfaces are selected and used as a starting point for fuzz test case generation. This paper presents work in progress.