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dc.contributor.authorMacDonell, Stephenen_NZ
dc.contributor.authorGray, Andrewen_NZ
dc.contributor.authorMacLennan, Granten_NZ
dc.contributor.authorSallis, Philipen_NZ
dc.date.available2011-04-07T03:05:01Z
dc.date.copyright1999-06en_NZ
dc.identifier.citationMacDonell, S., Gray, A., MacLennan, G., & Sallis, P. (1999). Software forensics for discriminating between program authors using case-based reasoning, feed-forward neural networks and multiple discriminant analysis (Information Science Discussion Papers Series No. 99/12). University of Otago. Retrieved from http://hdl.handle.net/10523/817en
dc.identifier.urihttp://hdl.handle.net/10523/817
dc.description.abstractSoftware forensics is a research field that, by treating pieces of program source code as linguistically and stylistically analyzable entities, attempts to investigate aspects of computer program authorship. This can be performed with the goal of identification, discrimination, or characterization of authors. In this paper we extract a set of 26 standard authorship metrics from 351 programs by 7 different authors. The use of feed-forward neural networks, multiple discriminant analysis, and case-based reasoning is then investigated in terms of classification accuracy for the authors on both training and testing samples. The first two techniques produce remarkably similar results, with the best results coming from the case-based reasoning models. All techniques have high prediction accuracy rates, supporting the feasibility of the task of discriminating program authors based on source-code measurements.en_NZ
dc.format.mimetypeapplication/pdf
dc.publisherUniversity of Otagoen_NZ
dc.relation.ispartofseriesInformation Science Discussion Papers Seriesen_NZ
dc.subject.lcshQA76 Computer softwareen_NZ
dc.titleSoftware forensics for discriminating between program authors using case-based reasoning, feed-forward neural networks and multiple discriminant analysisen_NZ
dc.typeDiscussion Paperen_NZ
dc.description.versionUnpublisheden_NZ
otago.bitstream.pages8en_NZ
otago.date.accession2010-11-10 20:34:56en_NZ
otago.schoolInformation Scienceen_NZ
otago.openaccessOpen
otago.place.publicationDunedin, New Zealanden_NZ
dc.identifier.eprints996en_NZ
otago.school.eprintsSoftware Metrics Research Laboratoryen_NZ
otago.school.eprintsInformation Scienceen_NZ
dc.description.references[1] A. Gray, P. Sallis, and S. MacDonell. Identified (integrated dictionary-based extraction of non-language-dependent token information for forensic identification, examination, and discrimination): A dictionary-based system for extracting source code metrics for software forensics. In Proceedings of SE:E&P’98 (Software Engineering: Education and Practice Conference), pages 252–259. IEEE Computer Society Press, 1998. [6] [2] I. Krsul and E. H. Spafford. Authorship analysis: Identifying the author of a program. Computers & Security, 16(3):233–256, 1997. [3] P. Sallis, A. Aakjaer, and S. MacDonell. Software forensics: Old methods for a new science. In Proceedings of SE:E&P’ 96 (Software Engineering: Education and Practice), pages 367–371. IEEE Computer Society Press, 1996. [4] P. Sallis, S. MacDonell, G. MacLennan, A. Gray, and R. Kilgour. Identified: Software authorship analysis with case-based reasoning. In Proceedings of the Addendum Session of the 1997 International Conference on Neural Information Processing and Intelligent Information Systems, pages 53– 56, 1998. [5] M. Shepperd and C. Schofield. Estimating software project effort using analogies. IEEE Transactions on Software Engineering, 23(11):736–743, 1997. [6] E. H. Spafford and S. A. Weeber. Software forensics: Can we track code to its authors? Computers & Security, 12:585–595, 1993. [7] G. Whale. Software metrics and plagiarism detection. Journal of Systems and Software, 13:131–138, 1990.en_NZ
otago.relation.number99/12en_NZ
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