Simulation of Software Reliability Growth Model Based on Fault Severity and Imperfect Debugging

2021 ◽  
pp. 140-152
Author(s):  
Xuejie Sun ◽  
Jiwei Li
Keyword(s):  
Growth Model ◽  
Software Reliability ◽  
Reliability Growth ◽  
Software Reliability Growth Model ◽  
Imperfect Debugging ◽  
Model Based ◽  
Fault Severity ◽  
Software Reliability Growth

AN NHPP SOFTWARE RELIABILITY GROWTH MODEL WITH IMPERFECT DEBUGGING AND ERROR GENERATION

2014 ◽  
Vol 21 (02) ◽  
pp. 1450008 ◽  
Author(s):  
P. ROY ◽  
G. S. MAHAPATRA ◽  
K. N. DEY
Keyword(s):  
Growth Model ◽  
Software Reliability ◽  
Data Sets ◽  
Reliability Growth ◽  
Software Reliability Growth Model ◽  
Imperfect Debugging ◽  
Failure Data ◽  
Proposed Model ◽  
Software Reliability Growth ◽  
Error Generation

In this paper, we propose a non-homogeneous Poisson process (NHPP) based software reliability growth model (SRGM) in the presence of modified imperfect debugging and fault generation phenomenon. The testing team may not be able to remove a fault perfectly on observation of a failure due to the complexity of software systems and incomplete understanding of software, and the original fault may remain, or get replaced by another fault causing error generation. We have proposed an exponentially increasing fault content function and constant fault detection rate. The total fault content of the software for our proposed model increases rapidly at the beginning of the testing process. It grows gradually at the end of the testing process because of increasing efficiency of the testing team with testing time. We use the maximum likelihood estimation method to estimate the unknown parameters of the proposed model. The applicability of our proposed model and comparisons with established models in terms of goodness of fit and predictive validity have been presented using five known software failure data sets. Experimental results show that the proposed model gives a better fit to the real failure data sets and predicts the future behavior of software development more accurately than the traditional SRGMs.

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