Testing coverage‐based software reliability growth model considering uncertainty of operating environment

Abstract

Software reliability is one of the standard critical inherent characteristics of software systems. The testing coverage function (TCF) is a significant parameter for identifying the completeness and effectiveness of software testing. It is defined as the proportion of the code that has been tested up to time t. To capture the dynamic behavior of the number of faults detected over a period of time, several distributions, namely S‐shaped, inflection S‐shaped, logistic, log‐logistic, Weibull, Rayleigh, Erlang, and logarithmic exponentiated, have been used as TCF in literature. However, these distributions are not sufficient to describe TCF's practical behavior due to complexity and vagueness in the collected data. This study proposes two software reliability growth models (SRGMs), which incorporate the generalized inflection S‐shaped (GISS) distribution as TCF. The models have been developed in perfect and imperfect debugging environments while considering fault removal efficiency, error generation, and uncertainty in the operating environment. To analyze the effectiveness, the proposed models are then tested with six failure data sets. The choice of GISS distribution as a TCF improves the software reliability estimation in comparison with the existing models in the literature. Finally, single and multiple parameters sensitivity analysis also has been done and based on it, the critical parameters have been detected. The proposed models may be helpful for the system analyst to predict various parameters about some software systems.

Study of a New Software Reliability Growth Model under Uncertain Operating Environments and Dependent Failures

“…So, looking at Equation (7), the uncertain operating environments are represented by η, the random variable. The assumption of dependent failure occurrences is represented by the parameters of the gamma distribution followed by η, which will be discussed in detail when explaining Equation (10).…”

“…The proposed model has five parameters, namely b, c, α, β, and N. In Equation (10), the parameters α and β are also the parameters of g(η…”

“…Different criteria have been suggested for evaluating how well a model fits the data [9]. This study discusses 10 criteria (MSE, PRR, PP, SAE, R 2 , AIC, PRV, RMSPE, MAE, and MEOP) to compare the proposed model with 10 existing NHPP SRGMs.…”

“…This encompasses various scenarios and possibilities. Pradhan et al [7][8][9][10] developed a model that incorporated an S-shaped inflection as the testing coverage function and considered uncertain operating environments. Environment factors (EFs) refer to various internal and external environments such as testing environments, programming tools, programming effort, program structure, hardware specifications, etc.…”

A Software Reliability Model Considering a Scale Parameter of the Uncertainty and a New Criterion

“…Because software is used in many different ways, its roles have become very diverse, and the environment provided to each piece of software has also become very diverse. Many studies have been conducted on SRMs that assume uncertainty in operating environments [17][18][19][20][21][22][23][24][25][26]. Here, uncertain operating environments refer to the environments used by actual consumers.…”