Improving the Classification of Software Behaviors using Ensembles

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Title: Improving the Classification of Software Behaviors using Ensembles
Author: Bowring, James Frederick ; Harrold, Mary Jean ; Rehg, James Matthew
Abstract: One approach to the automatic classification of program behaviors is to view these behaviors as the collection of all the program's executions. Many features of these executions, such as branch profiles, can be measured, and if these features accurately predict behavior, we can build automatic behavior classifiers from them using statistical machine-learning techniques. Two key problems in the development of useful classifiers are (1) the costs of collecting and modeling data and (2) the adaptation of classifiers to new or unknown behaviors. We address the first problem by concentrating on the properties and costs of individual features and the second problem by using the active-learning paradigm. In this paper, we present our technique for modeling a data-flow feature as a stochastic process exhibiting the Markov property. We introduce the novel concept of databins to summarize, as Markov models, the transitions of values for selected variables. We show by empirical studies that databin-based classifiers are effective. We also describe ensembles of classifiers and how they can leverage their components to improve classification rates. We show by empirical studies that ensembles of control-flow and data-flow based classifiers can be more effective than either component classifier.
Type: Technical Report
Date: 2005
Relation: CERCS;GIT-CERCS-05-10
Publisher: Georgia Institute of Technology
Subject: Active learning paradigms
Artificial intelligence
Automatic classification of program behaviors
Data bins
Machine learning
Markov models
Mathematics of computing
Probability and statistics
Program executions
Software behavior
Software engineering
Software testing
Software/program verification
Stochastic processes

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