Abstract
Due to high cost of fixing failures, safety concerns, and legal liabilities, organizations need to produce software that is highly reliable. Software reliability growth models have been developed by software developers in tracking and measuring the growth of reliability. Most of the Software Reliability Growth Models, which have been proposed, treat the event of software fault detection in the testing and operational phase as a counting process. Moreover, if the size of software system is large, the number of software faults detected during the testing phase becomes large, and the change of the number of faults which are detected and removed through debugging activities becomes sufficiently small compared with the initial fault content at the beginning of the testing phase. Therefore in such a situation, we can model the software fault detection process as a stochastic process with a continuous state space. Recently, Artificial Neural Networks (ANN) have been applied in software reliability growth prediction. In this paper, we propose an ANN based software reliability growth model based on Ito type of stochastic differential equation. The model has been validated, evaluated and compared with other existing NHPP model by applying it on actual failure/fault removal data sets cited from real software development projects. The proposed model integrated with the concept of stochastic differential equation performs comparatively better than the existing NHPP based model.
Highlights
We propose an Artificial Neural Networks (ANN) based software reliability growth model based on Ito type of stochastic differential equation
This paper presents an Software Reliability Growth Model (SRGM) based on Ito type Stochastic Differential Equations using ANN approach
The goodness of the fit analysis has been done on two real software failure datasets
Summary
There are numerous instances where failures of computercontrolled systems have led to colossal loss of human lives and money. Several Software Reliability models have been discussed in the literature Most of these are based upon historical failure data collected during the testing phase. These models have been utilized to evaluate the quality of the software and for future reliability predictions. The Software Reliability Growth Model (SRGM) is a tool of SRE that can be used to evaluate the software quantitatively, develop test status, schedule status and monitor the changes in reliability performance [1,2]. Yamada et al [4] proposed a simple software reliability growth model to describe the fault detection process during the testing phase by applying Ito type Stochastic Differential Equation (SDE) and obtain several software reliability measures using the probability distribution of the stochastic process. Lee et al [5] used SDE to represent a per-fault detection rate that incorporate an irregular fluctuation instead of an NHPP, and consider a per-fault detection rate that depends on the testing time t
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