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VeriFuzz is a program aware fuzz testing tool, which combines the power of feedback-driven evolutionary fuzz testing with static analysis. VeriFuzz deploys lightweight static analysis to extract meaningful information about program behavior that can aid fuzzing based testinput generation to achieve coverage goals quickly. We use constraintsolver to generate an initial population of test-inputs. VeriFuzz could generate the maximum number of counterexamples for reachsafety category benchmarks in SV-COMP 2019 and in Test-Comp 2019 [16]. (All the terms in typewriter font are competition specific. See [15].

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In this paper we propose a new family of algorithms, ATENT, for training adversarially robust deep neural networks. We formulate a new loss function that is equipped with an additional entropic regularization. Our loss function considers the contribution of adversarial samples that are drawn from a specially designed distribution in the data space that assigns high probability to points with high loss and in the immediate neighborhood of training samples. Our proposed algorithms optimize this loss to seek adversarially robust valleys of the loss landscape. Our approach achieves competitive (or better) performance in terms of robust classification accuracy as compared to several state-of-the-art robust learning approaches on benchmark datasets such as MNIST and CIFAR-10.

Logic synthesis is a challenging and widely-researched combinatorial optimization problem during integrated circuit (IC) design. It transforms a high-level description of hardware in a programming language like Verilog into an optimized digital circuit netlist, a network of interconnected Boolean logic gates, that implements the function. Spurred by the success of ML in solving combinatorial and graph problems in other domains, there is growing interest in the design of MLguided logic synthesis tools. Yet, there are no standard datasets or prototypical learning tasks defined for this problem domain. Here, we describe OpenABC-D, a large-scale, labeled dataset produced by synthesizing open source designs with a leading open-source logic synthesis tool and illustrate its use in developing, evaluating and benchmarking ML-guided logic synthesis. OpenABC-D has intermediate and final outputs in the form of 870,000 And-Inverter-Graphs (AIGs) produced from 1500 synthesis runs plus labels such as the optimized node counts, and delay. We define a generic learning problem on this dataset and benchmark existing solutions for it. The codes related to dataset creation and benchmark models are available at https://github.com/NYU-MLDA/OpenABC.git. The dataset generated is available at https://archive.nyu.edu/handle/2451/63311. 1 Σ 2 p -Hard problems are hard even with access to an oracle solver for NP-complete problems.