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dc.contributor.authorMolina, Facundo
dc.contributor.authorDegiovanni, Renzo
dc.contributor.authorPonzio, Pablo
dc.contributor.authorRegis, Germán
dc.contributor.authorAguirre, Nazareno
dc.contributor.authorFrías, Marcelo
dc.date.accessioned2020-03-19T15:44:28Z
dc.date.available2020-03-19T15:44:28Z
dc.date.issued2019
dc.identifier.issn02705257
dc.identifier.urihttp://ri.itba.edu.ar/handle/123456789/1911
dc.description.abstract"We present a technique to distinguish valid from invalid data structure objects. The technique is based on building an artificial neural network, more precisely a binary classifier, and training it to identify valid and invalid instances of a data structure. The obtained classifier can then be used in place of the data structure’s invariant, in order to attempt to identify (in)correct behaviors in programs manipulating the structure. In order to produce the valid objects to train the network, an assumed-correct set of object building routines is randomly executed. Invalid instances are produced by generating values for object fields that “break” the collected valid values, i.e., that assign values to object fields that have not been observed as feasible in the assumed-correct executions that led to the collected valid instances. We experimentally assess this approach, over a benchmark of data structures.We show that this learning technique produces classifiers that achieve significantly better accuracy in classifying valid/invalid objects compared to a technique for dynamic invariant detection, and leads to improved bug finding."en
dc.language.isoenen
dc.relationinfo:eu-repo/semantics/altIdentifier/doi/10.1109/ICSE.2019.00084
dc.relationinfo:eu-repo/grantAgreement/ANPCyT/PICT/2015-2341/AR. Ciudad Autónoma de Buenos Aires
dc.relationinfo:eu-repo/grantAgreement/ANPCyT/PICT/2015-0586/AR. Ciudad Autónoma de Buenos Aires
dc.relationinfo:eu-repo/grantAgreement/ANPCyT/PICT/2015-2088/AR. Ciudad Autónoma de Buenos Aires
dc.relationinfo:eu-repo/grantAgreement/ANPCyT/PICT/2016-1384/AR. Ciudad Autónoma de Buenos Aires
dc.relationinfo:eu-repo/grantAgreement/ANPCyT/PICT/2017-1979/AR. Ciudad Autónoma de Buenos Aires
dc.relationinfo:eu-repo/grantAgreement/ANPCyT/PICT/2017-2622/AR. Ciudad Autónoma de Buenos Aires
dc.relationinfo:eu-repo/grantAgreement/ANR/18/12632675/SATOCROSS/FR. Paris
dc.titleTraining binary classifiers as data structure invariantsen
dc.typePonencias en Congresoses
dc.typeinfo:eu-repo/semantics/acceptedVersion
itba.description.filiationFil: Molina,Facundo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
itba.description.filiationFil: Molina,Facundo. Universidad Nacional de Río Cuarto; Argentina.
itba.description.filiationFil: Degiovanni, Renzo. Universidad de Luxemburgo; Luxemburgo.
itba.description.filiationFil: Regis, Germán. Universidad Nacional de Río Cuarto; Argentina.
itba.description.filiationFil: Ponzio,Pablo. Universidad Nacional de Río Cuarto; Argentina.
itba.description.filiationFil: Ponzio,Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
itba.description.filiationFil: Aguirre, Nazareno. Universidad Nacional de Río Cuarto; Argentina.
itba.description.filiationFil: Aguirre, Nazareno. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
itba.description.filiationFil: Frias,Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
itba.description.filiationFil: Frias,Marcelo. Instituto Tecnológico de Buenos Aires; Argentina.


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