Paper published in a book (Scientific congresses, symposiums and conference proceedings)
Synthetic Data Generation for Statistical Testing
Soltana, Ghanem; Sabetzadeh, Mehrdad; Briand, Lionel
2017In 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE'17)
Peer reviewed
 

Files


Full Text
ASE17.pdf
Author postprint (1.06 MB)
Download

All documents in ORBilu are protected by a user license.

Send to



Details



Keywords :
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 center :
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Software Verification and Validation Lab (SVV Lab)
Disciplines :
Computer science
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)
External co-authors :
no
Language :
English
Title :
Synthetic Data Generation for Statistical Testing
Publication date :
2017
Event name :
32nd IEEE/ACM International Conference on Automated Software Engineering (ASE'17)
Event place :
Illinois, United States
Event date :
from 30-10-2017 to 03-11-2017
Audience :
International
Main work title :
32nd IEEE/ACM International Conference on Automated Software Engineering (ASE'17)
Publisher :
IEEE
ISBN/EAN :
978-1-5386-2684-9
Pages :
872-882
Peer reviewed :
Peer reviewed
Focus Area :
Security, Reliability and Trust
European Projects :
H2020 - 694277 - TUNE - Testing the Untestable: Model Testing of Complex Software-Intensive Systems
Funders :
CE - Commission Européenne [BE]
Available on ORBilu :
since 14 August 2017

Statistics


Number of views
279 (26 by Unilu)
Number of downloads
1300 (19 by Unilu)

Scopus citations®
 
35
Scopus citations®
without self-citations
33
WoS citations
 
27

Bibliography


Similar publications



Contact ORBilu