Reference : Evolutionary Robustness Testing of Data Processing Systems using Models and Data Mutation
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/21581
Evolutionary Robustness Testing of Data Processing Systems using Models and Data Mutation
English
Di Nardo, Daniel mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Pastore, Fabrizio mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Arcuri, Andrea mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Briand, Lionel mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > > ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)]
Nov-2015
Proceedings of the 30th IEEE/ACM International Conference on Automated Software Engineering
126-137
Yes
No
International
30th IEEE/ACM International Conference on Automated Software Engineering (ASE 2015)
November 9–13, 2015
Lincoln, Nebraska
USA
[en] System level testing of industrial data processing software poses several challenges. Input data can be very large, even in the order of gigabytes, and with complex constraints that define when an input is valid. Generating the right input data to stress the system for robustness properties (e.g. to test how faulty data is handled) is hence very complex, tedious and error prone when done manually. Unfortunately, this is the current practice in industry. In previous work, we defined a methodology to model the structure and the constraints of input data by using UML class diagrams and OCL constraints. Tests were automatically derived to cover predefined fault types in a fault model. In this paper, to obtain more effective system level test cases, we developed a novel search-based test generation tool. Experiments on a real-world, large industrial data processing system show that our automated approach can not only achieve better code coverage, but also accomplishes this using significantly smaller test suites.
University of Luxembourg: High Performance Computing - ULHPC
http://hdl.handle.net/10993/21581
FnR ; FNR4082113 > Daniel Di Nardo > > Regression test suite management strategies for web applications > 01/05/2012 > 30/04/2016 > 2012

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