Test Case Prioritization Based on Artificial Immune Algorithm

Regression testing is an essential and critical part of smart terminal program development. The test case suite is usually preprocessed by test case prioritization technology to improve the efficiency of regression testing. To address the problems of traditional genetic algorithm in solving the test...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Hongwei Xu, Pengcheng Li, Zhongxiao Cong, Fengzhi Zhang, Yi Pan, Xu Ren, Xingde Wang*, Ying Xing
Formato: article
Lenguaje:EN
Publicado: Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek 2021
Materias:
Acceso en línea:https://doaj.org/article/9d74fbe7042f41ceb5492bcd37e1a35d
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
id oai:doaj.org-article:9d74fbe7042f41ceb5492bcd37e1a35d
record_format dspace
spelling oai:doaj.org-article:9d74fbe7042f41ceb5492bcd37e1a35d2021-11-07T00:34:11ZTest Case Prioritization Based on Artificial Immune Algorithm1330-36511848-6339https://doaj.org/article/9d74fbe7042f41ceb5492bcd37e1a35d2021-01-01T00:00:00Zhttps://hrcak.srce.hr/file/383550https://doaj.org/toc/1330-3651https://doaj.org/toc/1848-6339Regression testing is an essential and critical part of smart terminal program development. The test case suite is usually preprocessed by test case prioritization technology to improve the efficiency of regression testing. To address the problems of traditional genetic algorithm in solving the test case prioritization problem, this paper proposed a test case prioritization algorithm for intelligent terminal based on artificial immune algorithm. Firstly, different sequences of test case sets were used as the encoding of antibodies to initialize the antibody population; secondly, the Hemming distance was introduced as the concentration index of antibodies to calculate the incentive degree; finally, the antibodies were immunized to find the optimal test case set sequence. The experimental results showed that the algorithm based on the artificial immune algorithm was more capable of global search and less likely to fall into local optimum than the genetic algorithm, which indicated that the artificial immune algorithm was more stable and could better solve the test case prioritization problem.Hongwei XuPengcheng LiZhongxiao CongFengzhi ZhangYi PanXu RenXingde Wang*Ying XingFaculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek articleartificial immunity algorithmsintelligent terminalregression testingtest case prioritizationEngineering (General). Civil engineering (General)TA1-2040ENTehnički Vjesnik, Vol 28, Iss 6, Pp 1871-1876 (2021)
institution DOAJ
collection DOAJ
language EN
topic artificial immunity algorithms
intelligent terminal
regression testing
test case prioritization
Engineering (General). Civil engineering (General)
TA1-2040
spellingShingle artificial immunity algorithms
intelligent terminal
regression testing
test case prioritization
Engineering (General). Civil engineering (General)
TA1-2040
Hongwei Xu
Pengcheng Li
Zhongxiao Cong
Fengzhi Zhang
Yi Pan
Xu Ren
Xingde Wang*
Ying Xing
Test Case Prioritization Based on Artificial Immune Algorithm
description Regression testing is an essential and critical part of smart terminal program development. The test case suite is usually preprocessed by test case prioritization technology to improve the efficiency of regression testing. To address the problems of traditional genetic algorithm in solving the test case prioritization problem, this paper proposed a test case prioritization algorithm for intelligent terminal based on artificial immune algorithm. Firstly, different sequences of test case sets were used as the encoding of antibodies to initialize the antibody population; secondly, the Hemming distance was introduced as the concentration index of antibodies to calculate the incentive degree; finally, the antibodies were immunized to find the optimal test case set sequence. The experimental results showed that the algorithm based on the artificial immune algorithm was more capable of global search and less likely to fall into local optimum than the genetic algorithm, which indicated that the artificial immune algorithm was more stable and could better solve the test case prioritization problem.
format article
author Hongwei Xu
Pengcheng Li
Zhongxiao Cong
Fengzhi Zhang
Yi Pan
Xu Ren
Xingde Wang*
Ying Xing
author_facet Hongwei Xu
Pengcheng Li
Zhongxiao Cong
Fengzhi Zhang
Yi Pan
Xu Ren
Xingde Wang*
Ying Xing
author_sort Hongwei Xu
title Test Case Prioritization Based on Artificial Immune Algorithm
title_short Test Case Prioritization Based on Artificial Immune Algorithm
title_full Test Case Prioritization Based on Artificial Immune Algorithm
title_fullStr Test Case Prioritization Based on Artificial Immune Algorithm
title_full_unstemmed Test Case Prioritization Based on Artificial Immune Algorithm
title_sort test case prioritization based on artificial immune algorithm
publisher Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
publishDate 2021
url https://doaj.org/article/9d74fbe7042f41ceb5492bcd37e1a35d
work_keys_str_mv AT hongweixu testcaseprioritizationbasedonartificialimmunealgorithm
AT pengchengli testcaseprioritizationbasedonartificialimmunealgorithm
AT zhongxiaocong testcaseprioritizationbasedonartificialimmunealgorithm
AT fengzhizhang testcaseprioritizationbasedonartificialimmunealgorithm
AT yipan testcaseprioritizationbasedonartificialimmunealgorithm
AT xuren testcaseprioritizationbasedonartificialimmunealgorithm
AT xingdewang testcaseprioritizationbasedonartificialimmunealgorithm
AT yingxing testcaseprioritizationbasedonartificialimmunealgorithm
_version_ 1718443628284608512