Fuzz testing, or fuzzing, is a black-box testing technique that has recently leapt to prominence as a quick and cost effective method for uncovering security bugs. Over 70 % of security vulnerabilities Microsoft patched in 2006 were found by fuzzing 1. This approach involves randomly mutating well

Black-box mutational fuzzing is a simple yet effective tech-nique to find bugs in software. Given a set of program-seed pairs, we ask how to schedule the fuzzings of these pairs in order to maximize the number of unique bugs found at any point in time. We develop an analytic framework using a

Abstract—We present the design of an algorithm to maximize the number of bugs found for black-box mutational fuzzing given a program and a seed input. The major intuition is to leverage white-box symbolic analysis on an execution trace for a given program-seed pair to detect dependencies among

Regression testing, which plays an important role in software maintenance, usually relies on test adequacy criteria to select and prioritize test cases. However, with the wide use and reuse of black-box components, such as reusable class libraries and COTS components, it is challenging to establish

as a “black box”. As an example of the approach, in this paper we consider a specific class of parallel computa-tions, nested-parallel computations. Such computations dynamically create a nesting of parallel tasks. We propose a method for organizing memory as a tree of heaps reflecting the structure

de nationalité suisse et originaire de Zurich (ZH) et Lucerne (LU) acceptée sur proposition du jury: