White-box test case generation based on improved genetic algorithm

Peng Wang; Xiao Juan Hu; Ning Jia Qiu; Hua Min Yang

doi:10.1007/978-3-642-25789-6_66

White-box test case generation based on improved genetic algorithm

Peng Wang, Xiao Juan Hu, Ning Jia Qiu, Hua Min Yang

Research output: Contribution to journal › Conference article › peer-review

1 Scopus citations

Abstract

Some intermittent or transient failures are particularly difficult to diagnose in highly complex and interconnected systems. This paper focuses on the use of genetic algorithms for automatically generating software test cases. In particular, this research extends a newly improved genetic algorithm, which adopts back propagation algorithm for local fine-tuning in the final link, and speeds up access to the best population. The various approaches offer opportunities for performance improvements that make these techniques more scalable for realistic applications.

Original language	English
Pages (from-to)	489-495
Number of pages	7
Journal	Lecture Notes in Electrical Engineering
Volume	125 LNEE
Issue number	VOL. 2
DOIs	https://doi.org/10.1007/978-3-642-25789-6_66
State	Published - 2012
Externally published	Yes
Event	2009 11th IEEE International Conference on e-Health Networking, Applications and Services, Healthcom 2009 - Sydney, Australia Duration: 16 Dec 2009 → 18 Dec 2009

Access to Document

10.1007/978-3-642-25789-6_66

Cite this

@article{9ad7de97dbf845dd984486262ccf0b8a,

title = "White-box test case generation based on improved genetic algorithm",

abstract = "Some intermittent or transient failures are particularly difficult to diagnose in highly complex and interconnected systems. This paper focuses on the use of genetic algorithms for automatically generating software test cases. In particular, this research extends a newly improved genetic algorithm, which adopts back propagation algorithm for local fine-tuning in the final link, and speeds up access to the best population. The various approaches offer opportunities for performance improvements that make these techniques more scalable for realistic applications.",

author = "Peng Wang and Hu, {Xiao Juan} and Qiu, {Ning Jia} and Yang, {Hua Min}",

year = "2012",

doi = "10.1007/978-3-642-25789-6_66",

language = "English",

volume = "125 LNEE",

pages = "489--495",

journal = "Lecture Notes in Electrical Engineering",

issn = "1876-1100",

publisher = "Springer Verlag",

number = "VOL. 2",

note = "2009 11th IEEE International Conference on e-Health Networking, Applications and Services, Healthcom 2009 ; Conference date: 16-12-2009 Through 18-12-2009",

}

TY - JOUR

T1 - White-box test case generation based on improved genetic algorithm

AU - Wang, Peng

AU - Hu, Xiao Juan

AU - Qiu, Ning Jia

AU - Yang, Hua Min

PY - 2012

Y1 - 2012

N2 - Some intermittent or transient failures are particularly difficult to diagnose in highly complex and interconnected systems. This paper focuses on the use of genetic algorithms for automatically generating software test cases. In particular, this research extends a newly improved genetic algorithm, which adopts back propagation algorithm for local fine-tuning in the final link, and speeds up access to the best population. The various approaches offer opportunities for performance improvements that make these techniques more scalable for realistic applications.

AB - Some intermittent or transient failures are particularly difficult to diagnose in highly complex and interconnected systems. This paper focuses on the use of genetic algorithms for automatically generating software test cases. In particular, this research extends a newly improved genetic algorithm, which adopts back propagation algorithm for local fine-tuning in the final link, and speeds up access to the best population. The various approaches offer opportunities for performance improvements that make these techniques more scalable for realistic applications.

UR - http://www.scopus.com/inward/record.url?scp=84880671151&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-25789-6_66

DO - 10.1007/978-3-642-25789-6_66

M3 - Conference article

AN - SCOPUS:84880671151

SN - 1876-1100

VL - 125 LNEE

SP - 489

EP - 495

JO - Lecture Notes in Electrical Engineering

JF - Lecture Notes in Electrical Engineering

IS - VOL. 2

T2 - 2009 11th IEEE International Conference on e-Health Networking, Applications and Services, Healthcom 2009

Y2 - 16 December 2009 through 18 December 2009

ER -

White-box test case generation based on improved genetic algorithm

Abstract

Access to Document

Fingerprint

Cite this