Abstract
Several software reliability growth models (SRGMs) have been developed by software developers in tracking and measuring the growth of reliability. As the size of software system is large and the number of faults detected during the testing phase becomes large, so the change of the number of faults that are detected and removed through each debugging becomes sufficiently small compared with the initial fault content at the beginning of the testing phase. In such a situation, we can model the software fault detection process as a stochastic process with continuous state space. In this paper, we propose a new software reliability growth model based on Itô type of stochastic differential equation. We consider an SDE‐based generalized Erlang model with logistic error detection function. The model is estimated and validated on real‐life data sets cited in literature to show its flexibility. The proposed model integrated with the concept of stochastic differential equation performs comparatively better than the existing NHPP‐based models.
Highlights
Software reliability engineering is a fast growing field
This paper presents an SRGM for different categories of faults based on Itotype Stochastic Differential Equations
We have extended the SDE approach adopted by Yamada et al 12 to the case where the faults are simple, hard, and complex in nature
Summary
Software reliability engineering is a fast growing field. More than 60% of critical applications are dependent on software. In the last two decades several Software Reliability models have been developed in the literature showing that the relationship between the testing time and the corresponding number of faults removed is either Exponential or Sshaped or a mix of the two 1–7. Ohba 6 refined the Goel-Okumoto model by assuming that the fault detection/removal rate increases with time and that there are two types of faults in the software. The total removal phenomenon is again modeled by the superposition of the three SRGMs 1, 8 Later they extended their model to cater for more types of faults 9 by incorporating logistic rate during the removal process. Yamada et al proposed a simple software reliability growth model to describe the fault detection process during the testing phase by applying Itotype Stochastic Differential Equation SDE and obtain several software reliability measures using the probability distribution of the stochastic process.
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