Reference : Synthetic Data Generation for Statistical Testing
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Security, Reliability and Trust
http://hdl.handle.net/10993/31878
Synthetic Data Generation for Statistical Testing
English
Soltana, Ghanem mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Sabetzadeh, Mehrdad 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) > >]
2017
32nd IEEE/ACM International Conference on Automated Software Engineering (ASE 2017)
10
Yes
No
International
32nd IEEE/ACM International Conference on Automated Software Engineering (ASE 2017)
from 30-10-2017 to 03-11-2017
University of Illinois at Urbana-Champaign
Illinois
USA
[en] Test Data Generation ; Usage-based Statistical Testing ; Model-Driven Engineering ; UML ; OCL
[en] Usage-based statistical testing employs knowledge about the actual or anticipated usage profile of the system under test for estimating system reliability. For many systems, usage-based statistical testing involves generating synthetic test data. Such data must possess the same statistical characteristics as the actual data that the system will process during operation. Synthetic test data must further satisfy any logical validity constraints that the actual data is subject to. Targeting data-intensive systems, we propose an approach for generating synthetic test data that is both statistically representative and logically valid. The approach works by first generating a data sample that meets the desired statistical characteristics, without taking into account the logical constraints. Subsequently, the approach tweaks the generated sample to fix any logical constraint violations. The tweaking process is iterative and continuously guided toward achieving the desired statistical characteristics. We report on a realistic evaluation of the approach, where we generate a synthetic population of citizens' records for testing a public administration IT system. Results suggest that our approach is scalable and capable o
Researchers ; Professionals ; Students ; Others
http://hdl.handle.net/10993/31878
H2020 ; 694277 - TUNE - Testing the Untestable: Model Testing of Complex Software-Intensive Systems

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