We propose a novel approach for statistical risk modeling of network attacks that lets an operator perform risk analysis using a data model and an impact model on top of an attack ...
Abstract— Process variations make at-speed testing significantly more difficult. They cause subtle delay changes that are distributed rather than the localized nature of a trad...
Vladimir Zolotov, Jinjun Xiong, Hanif Fatemi, Chan...
Abstract. Statistical debugging uses machine learning to model program failures and help identify root causes of bugs. We approach this task using a novel Delta-Latent-Dirichlet-Al...
David Andrzejewski, Anne Mulhern, Ben Liblit, Xiao...
Abstract. As High Performance Computing becomes more collaborative, software certification practices are needed to quantify the credibility of shared applications. To demonstrate q...
d Abstract) Alexander Aiken1 and Edward L. Wimmers2 and Jens Palsberg3 1 EECS Department, University of California at Berkeley, Berkeley, CA 94720-1776. 2 IBM Almaden Research Cent...