DocumentCode :
2709167
Title :
Software reliability model by AGP
Author :
Zhang, Yongqiang ; Yin, Jingjie
Author_Institution :
Hebei Univ. of Eng., Handan
fYear :
2008
fDate :
21-24 April 2008
Firstpage :
1
Lastpage :
5
Abstract :
To solve the problems of the incongruence of software reliability models and cast off the traditional models´ multi-subjective assumptions, this paper adopts genetic programming evolution algorithm which has adaptive genetic operators (for short AGP) to establish software reliability model based on software failure time series. The individual of the population is according to the case of the fitness of the generation to adjust the probability of crossover and mutation by the sigmoid curve. By evaluating the data series of the software testing case in Armored Force Engineering Institute, the results sufficiently testify that the new AGP algorithm has better applicability and the validity of fitness and forecasting. Moreover, compared with standard genetic programming evolution algorithm, the new AGP algorithm has the better rapidity of convergence. Therefore, we can say that, this algorithm can be more effectively applied to software testing and ensured the validity of data.
Keywords :
genetic algorithms; program testing; software reliability; AGP algorithm; Armored Force Engineering Institute; adaptive genetic operators; genetic programming evolution algorithm; sigmoid curve; software failure time series; software reliability model; software testing; Convergence; Data engineering; Genetic mutations; Genetic programming; Life estimation; Phase estimation; Reliability engineering; Software algorithms; Software reliability; Software testing; GP; adaptive genetic operators; reliability model; software reliability;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Industrial Technology, 2008. ICIT 2008. IEEE International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-1705-6
Electronic_ISBN :
978-1-4244-1706-3
Type :
conf
DOI :
10.1109/ICIT.2008.4608638
Filename :
4608638
Link To Document :
بازگشت