Property-Driven Black-Box Testing of Numeric Functions

In this work, we propose a property-driven testing mechanism to perform unit testing of functions performing numerical computations. Our approach, similar to the property-based testing technique, allows the tester to specify the requirements to check. Unlike property-based testing, the specification is then used to generate test cases in a targeted manner. Moreover, our approach works as a black-box testing tool, i.e. it does not require knowledge about the internals of the function under test. Therefore, besides on programmed numeric functions, we also apply our technique to machine-learned regression models. The experimental evaluation on a number of case studies shows the effectiveness of our testing approach

Constructing Search Spaces for Search-Based Software Testing Using Neural Networks

A central requirement for any Search-Based Software Testing (SBST) technique is a convenient and meaningful fitness landscape. Whether one follows a targeted or a diversification driven strategy, a search landscape needs to be large, continuous, easy to construct and representative of the underlying property of interest. Constructing such a landscape is not a trivial task often requiring a significant manual effort by an expert. We present an approach for constructing meaningful and convenient fitness landscapes using neural networks (NN) – for targeted and diversification strategies alike. We suggest that output of an NN predictor can be interpreted as a fitness for a targeted strategy. The NN is trained on a corpus of execution traces and various properties of interest, prior to searching. During search, the trained NN is queried to predict an estimate of a property given an execution trace. The outputs of the NN form a convenient search space which is strongly representative of a number of properties. We believe that such a search space can be readily used for driving a search towards specific properties of interest. For a diversification strategy, we propose the use of an autoencoder; a mechanism for compacting data into an n-dimensional “latent” space. In it, datapoints are arranged according to the similarity of their salient features. We show that a latent space of execution traces possesses characteristics of a convenient search landscape: it is continuous, large and crucially, it defines a notion of similarity to arbitrary observations

Making the Most of United States v. Jones in a Surveillance Society: A Statutory Implementation of Mosaic Theory

article published in law journalIn the Supreme Court's recent decision in United States v. Jones, a majority of the Justices appeared to recognize that under some circumstances aggregation of information about an individual through governmental surveillance can amount to a Fourth Amendment search. If adopted by the Court, this notion sometimes called "mosaic theory"-could bring about a radical change to Fourth Amendment jurisprudence, not just in connection with surveillance of public movements-the issue raised in Jonesbut also with respect to the government's increasingly pervasive record-mining efforts. One reason the Court might avoid the mosaic theory is the perceived difficulty of implementing it. This article provides, in the guise of a model statute, a means of doing so. More specifically, this article explains how proportionality reasoning and political process theory can provide concrete guidance for the courts and police in connection with physical and data surveillance

Defense against Universal Adversarial Perturbations

Recent advances in Deep Learning show the existence of image-agnostic quasi-imperceptible perturbations that when applied to `any' image can fool a state-of-the-art network classifier to change its prediction about the image label. These `Universal Adversarial Perturbations' pose a serious threat to the success of Deep Learning in practice. We present the first dedicated framework to effectively defend the networks against such perturbations. Our approach learns a Perturbation Rectifying Network (PRN) as `pre-input' layers to a targeted model, such that the targeted model needs no modification. The PRN is learned from real and synthetic image-agnostic perturbations, where an efficient method to compute the latter is also proposed. A perturbation detector is separately trained on the Discrete Cosine Transform of the input-output difference of the PRN. A query image is first passed through the PRN and verified by the detector. If a perturbation is detected, the output of the PRN is used for label prediction instead of the actual image. A rigorous evaluation shows that our framework can defend the network classifiers against unseen adversarial perturbations in the real-world scenarios with up to 97.5% success rate. The PRN also generalizes well in the sense that training for one targeted network defends another network with a comparable success rate.Comment: Accepted in IEEE CVPR 201

Healing Our Houses Will Cure Lead Poisoning Epidemic

Two main obstacles hinder efforts to end lead poisoning in Buffalo. One, lack of knowledge in at-risk populations about causes, symptoms, and prevention, which puts people at greater risk and makes enforcement of current system difficult. Two, the poor condition of our houses makes repairs unaffordable to homeowners and discourages outside investment