Comparing Fuzzers on a Level Playing Field with FuzzBench

Fuzzing is a testing approach commonly used in industry to discover bugs in a given software under test (SUT). It consists of running a SUT iteratively with randomly generated (or mutated) inputs, in order to find as many as possible inputs that make the SUT crash. Many fuzzers have been proposed to date, however no consensus has been reached on how to properly evaluate and compare fuzzers. In this work we evaluate and compare nine prominent fuzzers by carrying out a thorough empirical study based on an open-source framework developed by Google, namely FuzzBench, and a manually curated benchmark suite of 12 real-world software systems. The results show that honggfuzz and AFL++ are, in that order, the best choices in terms of general purpose fuzzing effectiveness. The results also show that none of the fuzzers outperforms the others in terms of efficiency across all considered metrics, that no particular bug affinity is found for any fuzzer, and that the correlation found between coverage and number of bugs depends more on the SUT rather than on the fuzzer used

Vehicular crowd-sensing: a parametric routing algorithm to increase spatio-temporal road network coverage

Current vehicles are equipped with a number of environmental sensors to improve safety and quality of life for passengers. Many researchers have shown that these sensors can also be exploited for opportunistic crowd-sensing. Useful new services can be developed on top of these data, like urban surveillance of Smart Cities. The spatio-temporal sensing coverage achievable with Vehicular Crowd-Sensing (VCS), however, is an open issue, since vehicles are not uniformly distributed over the road network, undermining the quality of potential services based on VCS data. In this paper, we present an evolution of the standard A∗ routing algorithm, meant to increase VCS coverage by selecting a route in a random way among all those satisfying a parametric constraint on the total cost of the path. The proposed solution is based on an edge-computing paradigm, not requiring a central coordination but rather leveraging the computational resources available on-board, significantly reducing the back-end infrastructure costs. The proposed solution has been empirically evaluated on two public datasets of 450,000 real taxi trajectories from two cities, San Francisco and Porto, characterized by a very different road network topology. Results show sensible improvements in terms of achievable spatio-temporal sensing coverage of probe vehicles