Reference : Training binary classifiers as data structure invariants
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Computational Sciences
Training binary classifiers as data structure invariants
Molina, Facundo [> >]
Degiovanni, Renzo Gaston mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) >]
Ponzio, Pablo [> >]
Regis, Germán [> >]
Aguirre, Nazareno [> >]
Frias, Marcelo F. [> >]
Proceedings of the 41st International Conference on Software Engineering ICSE 2019, Montreal, QC, Canada, May 25-31, 2019
759 - 770
41st International Conference on Software Engineering ICSE 2019
May 25-31, 2019
[en] We present a technique that enables us 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 program 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.
Researchers ; Professionals ; Students ; General public
FnR ; FNR12632675 > Michail Papadakis > SATOCROSS > Support of Advanced Test cOverage Criteria for RObust and Secure Software > 01/01/2019 > 31/12/2021 > 2018

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