Title:
Improving the Classification of Software Behaviors using Ensembles
Improving the Classification of Software Behaviors using Ensembles
| dc.contributor.author | Bowring, James Frederick | |
| dc.contributor.author | Harrold, Mary Jean | |
| dc.contributor.author | Rehg, James M. | |
| dc.date.accessioned | 2005-05-02T18:28:16Z | |
| dc.date.available | 2005-05-02T18:28:16Z | |
| dc.date.issued | 2005 | |
| dc.description.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. | en |
| dc.format.extent | 319814 bytes | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/1853/6010 | |
| dc.language.iso | en_US | |
| dc.publisher | Georgia Institute of Technology | en |
| dc.relation.ispartofseries | CERCS;GIT-CERCS-05-10 | |
| dc.subject | Active learning paradigms | en |
| dc.subject | Artificial intelligence | en |
| dc.subject | Automatic classification of program behaviors | en |
| dc.subject | Data bins | en |
| dc.subject | Experimentation | en |
| dc.subject | Machine learning | en |
| dc.subject | Markov models | en |
| dc.subject | Mathematics of computing | en |
| dc.subject | Measurement | en |
| dc.subject | Modeling | en |
| dc.subject | Probability and statistics | en |
| dc.subject | Program executions | en |
| dc.subject | Reliability | en |
| dc.subject | Software behavior | en |
| dc.subject | Software engineering | en |
| dc.subject | Software testing | en |
| dc.subject | Software/program verification | en |
| dc.subject | Stochastic processes | en |
| dc.subject | Verification | en |
| dc.title | Improving the Classification of Software Behaviors using Ensembles | en |
| dc.type | Text | |
| dc.type.genre | Technical Report | |
| dspace.entity.type | Publication | |
| local.contributor.author | Harrold, Mary Jean | |
| local.contributor.author | Rehg, James M. | |
| local.contributor.corporatename | Center for Experimental Research in Computer Systems | |
| local.relation.ispartofseries | CERCS Technical Report Series | |
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