Use of cumulants to quantify uncertainties in the HBT measurements of the homogeneity regions

Let us denote p(x|K) the space density of the points where identical particles of some kind, e.g. pi+ mesons, with momentum K are produced. When using the HBT method to determine p(x|K) one encounters ambiguities. We show that these ambiguities do not affect the even cumulants of the distribution p(x|K). In particular, the HBT radii of the homogeneity regions, which are given by the second order cumulants, and the distribution of distances between the pairs of production points for particles with momentum K can be reliably measured. The odd cumulants are ambiguous. The are, however, correlated. In particular, when the average position (K) is known as a function of K there is no further ambiguity.Comment: LateX, 10 pages, no figure

Ambiguities in the HBT approach to determine the interaction regions

The necessary and sufficient condition for a quantity to be measurable by the HBT method is given and discussed.Comment: Report at the conference QCD08, July 2008, LateX 8 pages, no figure

FairFuzz: Targeting Rare Branches to Rapidly Increase Greybox Fuzz Testing Coverage

In recent years, fuzz testing has proven itself to be one of the most effective techniques for finding correctness bugs and security vulnerabilities in practice. One particular fuzz testing tool, American Fuzzy Lop or AFL, has become popular thanks to its ease-of-use and bug-finding power. However, AFL remains limited in the depth of program coverage it achieves, in particular because it does not consider which parts of program inputs should not be mutated in order to maintain deep program coverage. We propose an approach, FairFuzz, that helps alleviate this limitation in two key steps. First, FairFuzz automatically prioritizes inputs exercising rare parts of the program under test. Second, it automatically adjusts the mutation of inputs so that the mutated inputs are more likely to exercise these same rare parts of the program. We conduct evaluation on real-world programs against state-of-the-art versions of AFL, thoroughly repeating experiments to get good measures of variability. We find that on certain benchmarks FairFuzz shows significant coverage increases after 24 hours compared to state-of-the-art versions of AFL, while on others it achieves high program coverage at a significantly faster rate