Reference : Synthetic Data Generation for Statistical Testing

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	Focus Areas :	Security, Reliability and Trust
	To cite this reference:	http://hdl.handle.net/10993/31878

Title :	Synthetic Data Generation for Statistical Testing
Language :	English
Author, co-author :	Soltana, Ghanem [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Sabetzadeh, Mehrdad [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Briand, Lionel [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Publication date :	2017
Main document title :	32nd IEEE/ACM International Conference on Automated Software Engineering (ASE'17)
Publisher :	IEEE
Pages :	872-882
Peer reviewed :	Yes
On invitation :	No
Audience :	International
ISBN :	978-1-5386-2684-9
Event name :	32nd IEEE/ACM International Conference on Automated Software Engineering (ASE'17)
Event date :	from 30-10-2017 to 03-11-2017
Event place (city) :	Illinois
Event country :	USA
Keywords :	[en] Test Data Generation ; Usage-based Statistical Testing ; Model-Driven Engineering ; UML ; OCL
Abstract :	[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
Research centres :	Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Software Verification and Validation Lab (SVV Lab)
Target :	Researchers ; Professionals ; Students ; Others
Permalink :	http://hdl.handle.net/10993/31878
DOI :	10.1109/ASE.2017.8115698
Framework Programme / European Project :	H2020 ; 694277 - TUNE - Testing the Untestable: Model Testing of Complex Software-Intensive Systems

	File(s) associated to this reference
	Fulltext file(s): File Commentary Version Size Access Open access ASE17.pdf Author postprint 1.04 MB View/Open

All documents in ORBi^lu are protected by a user license.

 

University of Luxembourg Library |  Feedback |  Legal notices Site Map