An NHPP Software Reliability Model with S-Shaped Growth Curve Subject to Random Operating Environments and Optimal Release Time

Abstract

Abstract:The failure of a computer system because of a software failure can lead to tremendous losses to society; therefore, software reliability is a critical issue in software development. As software has become more prevalent, software reliability has also become a major concern in software development. We need to predict the fluctuations in software reliability and reduce the cost of software testing: therefore, a software development process that considers the release time, cost, reliability, and risk is indispensable. We thus need to develop a model to accurately predict the defects in new software products. In this paper, we propose a new non-homogeneous Poisson process (NHPP) software reliability model, with S-shaped growth curve for use during the software development process, and relate it to a fault detection rate function when considering random operating environments. An explicit mean value function solution for the proposed model is presented. Examples are provided to illustrate the goodness-of-fit of the proposed model, along with several existing NHPP models that are based on two sets of failure data collected from software applications. The results show that the proposed model fits the data more closely than other existing NHPP models to a significant extent. Finally, we propose a model to determine optimal release policies, in which the total software system cost is minimized depending on the given environment.

Exploring Ant Lion Optimization Algorithm to Enhance the Choice of an Appropriate Software Reliability Growth Model

“…Goel-Okumoto Model(Goel_O) [13], Gomperts Model(Gomperts) [3], Inflection S-Shaped Model(Inflection_S-S) [10], Logistic Growth Model(Logistic_G) [3], Modified Duane Model(Modified_D) [14], Musa Okumoto Model(Musa_O) [15], Yamada Imperfect Debugging Model1(Yamada_I_D Model1) [10], Delayed S-Shaped Model(Delayed_S-S) [11], Yamada Rayleigh Model(Yamada_R) [10], Yamada imperfect debugging Model2(Yamada_I_DModel2) [16], Yamada Exponential Model(Yamada_E) [10], P-N-Z Model(P_N_Z) [16], Pham Zhang IFD Model(Pham_Z_I) [14] ,P-Z Model(P_Z) [13] and Zhang-Teng-Pham Model(Zhang_T_P) [14].…”

“…Later We compare the Rsq and RMSE values of the models selected with a specific value according to (13,14), the specified value chosen as (0.95) for equation (13) because most of the criterion values of the models are good and close to one, and chosen as(10) for equation (14). From table (2) we conclude that Yamada Rayleigh (Yamada_R), Delayed S-shaped (Delayed_S-S) and Pham Zhang IFD (Pham_Z_I) models do not subject to the two preceding conditions in equation (13,14), So it will be neglected. For all Other models that are subject to the previous two conditions, we calculate the value of PRED from equation (15).…”

A Testing Coverage Model Based on NHPP Software Reliability Considering the Software Operating Environment and the Sensitivity Analysis

“…In the early 2000s, researchers began to explore new approaches such as the application of calibration factors to the software reliability model considering the uncertainty of the operating environment [11][12][13]. Recently, an NHPP software reliability model considering the uncertainty of the software operating environment has been proposed [14][15][16][17][18][19]. In addition, many testing coverage functions have been proposed in terms of different distributions, and software reliability models based on different testing coverage function have also been developed [2,[20][21][22].…”

A Software Reliability Model Considering the Syntax Error in Uncertainty Environment, Optimal Release Time, and Sensitivity Analysis

“…Li et al [12] performed new testing coverage modeling that considers not only the error generation but also the fault removal efficiency based on NHPP. Song et al [13][14][15] considered operating environments. They applied random variable to the software reliability model, which represent the uncertainty in the operating environment.…”