arXiv subjects

Sarfraz Khurshid

Publications and source records attributed to Sarfraz Khurshid.

22 records · Page 2Linked to original sources

arXiv2018

Symbolic Execution for Deep Neural Networks

Deep Neural Networks (DNN) are increasingly used in a variety of applications, many of them with substantial safety and security concerns. This paper introduces DeepCheck, a new approach for validating DNNs based on core ideas from program analysis, specifically from symbolic execution. The idea is to translate a DNN into an imperative program, thereby enabling program analysis to assist with DNN validation. A basic translation however creates programs that are very complex to analyze. DeepCheck introduces novel techniques for lightweight symbolic analysis of DNNs and applies them in the context of image classification to address two challenging problems in DNN analysis: 1) identification of important pixels (for attribution and adversarial generation); and 2) creation of 1-pixel and 2-pixel attacks. Experimental results using the MNIST data-set show that DeepCheck's lightweight symbolic analysis provides a valuable tool for DNN validation.

cs.SE↗

arXiv2018

Fault Localization for Declarative Models in Alloy

Fault localization is a popular research topic and many techniques have been proposed to locate faults in imperative code, e.g. C and Java. In this paper, we focus on the problem of fault localization for declarative models in Alloy -- a first order relational logic with transitive closure. We introduce AlloyFL, the first set of fault localization techniques for faulty Alloy models which leverages multiple test formulas. AlloyFL is also the first set of fault localization techniques at the AST node granularity. We implements in AlloyFL both spectrum-based and mutation-based fault localization techniques, as well as techniques that are based on Alloy's built-in unsat core. We introduce new metrics to measure the accuracy of AlloyFL and systematically evaluate AlloyFL on 38 real faulty models and 9000 mutant models. The results show that the mutation-based fault localization techniques are significantly more accurate than other types of techniques.

cs.SE↗

arXiv2018

DeepRoad: GAN-based Metamorphic Autonomous Driving System Testing

While Deep Neural Networks (DNNs) have established the fundamentals of DNN-based autonomous driving systems, they may exhibit erroneous behaviors and cause fatal accidents. To resolve the safety issues of autonomous driving systems, a recent set of testing techniques have been designed to automatically generate test cases, e.g., new input images transformed from the original ones. Unfortunately, many such generated input images often render inferior authenticity, lacking accurate semantic information of the driving scenes and hence compromising the resulting efficacy and reliability. In this paper, we propose DeepRoad, an unsupervised framework to automatically generate large amounts of accurate driving scenes to test the consistency of DNN-based autonomous driving systems across different scenes. In particular, DeepRoad delivers driving scenes with various weather conditions (including those with rather extreme conditions) by applying the Generative Adversarial Networks (GANs) along with the corresponding real-world weather scenes. Moreover, we have implemented DeepRoad to test three well-recognized DNN-based autonomous driving systems. Experimental results demonstrate that DeepRoad can detect thousands of behavioral inconsistencies in these systems.

cs.SE↗

arXiv2017

Effectiveness of Anonymization in Double-Blind Review

Double-blind review relies on the authors' ability and willingness to effectively anonymize their submissions. We explore anonymization effectiveness at ASE 2016, OOPSLA 2016, and PLDI 2016 by asking reviewers if they can guess author identities. We find that 74%-90% of reviews contain no correct guess and that reviewers who self-identify as experts on a paper's topic are more likely to attempt to guess, but no more likely to guess correctly. We present our findings, summarize the PC chairs' comments about administering double-blind review, discuss the advantages and disadvantages of revealing author identities part of the way through the process, and conclude by advocating for the continued use of double-blind review.

cs.DL↗