DocumentCode :
2417618
Title :
A study of software reliability growth model for time-dependent learning effects
Author :
Kuei-Chen Chiu
Author_Institution :
Dept. of Bus. Adm., Hsing Kuo Univ. of Manage., Taiwan
fYear :
2012
fDate :
10-13 Dec. 2012
Firstpage :
1015
Lastpage :
1019
Abstract :
This paper considered time-dependent learning effects in the software reliability growth model which Chiu et al. (2008) provided from the perspective of learning effects and would be able to reasonably describe the S-shaped and exponential-shaped types of behaviors simultaneously, and had better performance in fitting different data with consideration of a constant learning effect to enhance the model. This study assumed learning effects were depend on the process time and improved the model with linear-learning effect and exponential-learning effect to discuss when and what learning effects would occur in the software development process. This paper also verified the effectiveness of the proposed model with R square (Rsq) and compared with other models by using the comparison criteria with real data set. The results revealed that the proposed model shows good fitting in the data set which software development process exists time-dependent learning effects.
Keywords :
learning (artificial intelligence); program testing; software reliability; R square; Rsq; S-shaped behavior type; comparison criteria; constant learning effect; exponential-learning effect; exponential-shaped behavior types; linear-learning effect; process time; real data set; software development process; software reliability growth model; software testing; time-dependent learning effects; Analytical models; Computational modeling; Data models; Debugging; Mathematical model; Software; Software reliability; Non-Homogeneous Poisson Process (NHPP); Software reliability; learning effects; time-dependent learning effects;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Industrial Engineering and Engineering Management (IEEM), 2012 IEEE International Conference on
Conference_Location :
Hong Kong
Type :
conf
DOI :
10.1109/IEEM.2012.6837894
Filename :
6837894
Link To Document :
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