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 by a program dependence graph with estimates of statistical dependences between node states, which are computed from the test set. The PPDG is based on the established framework of probabilistic graphical models, which are widely used in applications such as medical diagnosis. This paper presents algorithms for constructing PPDGs and applying the PPDG to fault diagnosis. This paper also presents preliminary evidence indicating that PPDGs can facilitate fault localization and fault comprehension.
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 by a program dependence graph with estimates of statistical dependences between node states, which are computed from the test set. The PPDG is based on the established framework of probabilistic graphical models, which are widely used in applications such as medical diagnosis. This paper presents algorithms for constructing PPDGs and applying the PPDG to fault diagnosis. This paper also presents preliminary evidence indicating that PPDGs can facilitate fault localization and fault comprehension.
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 trained model is then deployed with the software to perform anomaly detection. By performing the anomaly detection as the software is executing, faults associated with anomalies can be located and fixed before they cause critical failures in the system, and developers time to debug deployed software can be reduced. This paper also presents a prototype implementation of our technique, along with a case study that shows, for the subjects we studied, the effectiveness of the technique.
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