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This paper presents an innovative model of a program's internal behavior over a set of test inputs, called the probabilistic program dependence graph (PPDG), that facilitates probabilistic analysis and reasoning about uncertain program behavior, particularly that associated with faults. The PPDG is an augmentation of the structural dependences represented… (More)

This paper investigates the application of <i>causal inference</i> methodology for observational studies to software fault localization based on test outcomes and profiles. This methodology combines statistical techniques for counterfactual inference with causal graphical models to obtain causal-effect estimates that are not subject to severe confounding… (More)

Dynamic program dependences are recognized as important factors in software debugging because they contribute to triggering the effects of faults and propagating the effects to a program's output. The effects of dynamic dependences also produce significant confounding bias when statistically estimating the causal effect of a statement on the occurrence of… (More)

This paper presents a new machine-learning technique that performs anomaly detection as software is executing in the field. The technique uses a fully observable Markov model where each state in the model emits a number of distinct observations according to a probability distribution, and estimates the model parameters using the Baum-Welch algorithm. The… (More)

Periodic randomization of a computer program's binary code is an attractive technique for defending against several classes of advanced threats. In this paper we describe a model of attacker-defender interaction in which the defender employs such a technique against an attacker who is actively constructing an exploit using Return Oriented Programming (ROP).… (More)

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