Fault Detection Rate Function Research Articles

Analysis of incorporating modified Weibull model fault detection rate function into software reliability modeling

When software systems are introduced, they are typically deployed in field environments similar to those used during development and testing. However, these systems may also be used in various other locations with different environmental conditions, making it challenging to improve software reliability. Factors such as the specific operating environment and the location of bugs in the code contribute to this difficulty. In this paper, we propose a new software reliability model that accounts for the uncertainty of operating environments. We present the explicit closed-form mean value function solution for the proposed model. The model's goodness of fit is demonstrated by comparing it to the nonhomogeneous Poisson process (NHPP) model based on Weibull model, using four sets of failure data sets from software applications. The proposed model performs well under various estimation techniques, making it a versatile tool for practitioners and researchers alike. The proposed model outperforms other existing NHPP Weibull based in terms of fitting accuracy under two different methods of estimation and provides a more detailed and precise evaluation of software reliability. Additionally, sensitivity analysis shows that the parameters of the suggested distribution significantly impact the mean value function.

Change point software belief reliability growth model considering epistemic uncertainties

Various software reliability growth models (SRGMs) have been developed to evaluate software reliability. Since software faults are caused by bugs in the logic of the code, which are introduced due to human errors, software faults embody lots of epistemic uncertainties. Under the framework of uncertainty theory, a software belief reliability growth model (SBRGM) was proposed. Actually, during software testing fault detection rate function may show multiple phases due to changes of testing strategies and resources allocation. Thus, fault detection rate function may be discontinuous at some time known as the change point. Noting that the previous SBRGM did not consider the change point, this paper construct a new SBRGM incorporating change point, named CPSBRGM. Based on the new proposed model, reliability for software is evaluated under the framework of belief reliability theory. Method to estimate unknown parameters in the proposed model is also provided. The new proposed model is validated on three datasets and compared with the previous SBRGM. Results show that the new proposed model can clearly distinguish the change point during software testing, and is more in line with the actual software test situation than previous model without considering change points.

An NHPP software reliability model with a fault detection rate function of the Burr type XII distribution

The software reliability model is developed considering the number of software failures and the interval time between failures, and is a very useful model for predicting software failures that may occur in the future. In most existing software reliability model, the mean value function is considered to follow a non-homogeneous Poisson process (NHPP). NHPP software reliability model has a mean value function that considers the failure intensity, detection rate, number of remaining failures, and various environments. The Burr distribution can fit a wide range of empirical data. In addition, it was proposed several types of the cumulative functions and has stressed the advantages obtained by the direct use of the cumulative function. The Burr type XII distribution is a continuous probability distribution for a non-negative random variable. In this paper, we propose a new software reliability model with a fault detection rate function of the Burr type XII distribution based on NHPP. We show that the NHPP software reliability model proposed through various criteria shows much better the goodness-of-fit than other existing NHPP software reliability models.

Default Detection Rate-Dependent Software Reliability Model with Imperfect Debugging

From the perspective of FDR (fault detection rate), which is an indispensable component in reliability modeling, this paper proposes two kinds of reliability models under imperfect debugging. This model is a relatively flexible and unified software reliability growth model. First, this paper examines the incomplete phenomenon of debugging and fault repair and established a unified imperfect debugging framework model related to FDR, which is called imperfect debugging type I. Furthermore, it considers the introduction of new faults during debugging and establishes a unified imperfect debugging framework model that supports multiple FDRs, called imperfect debugging type II. Finally, a series of specific reliability models are derived by integrating multiple specific FDRs into two types of imperfect debugging framework models. Based on the analysis of the two kinds of imperfect debugging models on multiple public failure data sets, and the analysis of model performance differences from the perspective of fitting metrics and prediction research, a fault detection rate function that can better describe the fault detection process is found. By incorporating this fault detection rate function into the two types of imperfect debugging models, a more accurate model is obtained, which not only has excellent performance and is superior to other models but also describes the real testing process more accurately and will guide software testers to quantitatively improve software reliability.

An effective software reliability growth model

Reliability is an end-user quality feature related to system usage. Software reliability modelling has gained a lot of importance due to the growing need for high-quality software. The paper presents a new Software reliability growth model (SRGM) following the structure of the logistic growth model. As testing progresses, the skill of testing team will improve which will lead to higher fault detection rate. We incorporate this factor with the help of a time-dependent fault detection rate function suggested in Generalised Goel model. The model is applied to three different widely used software failure datasets. Four different performance criteria namely MSE (Mean Square Error), Variance, PRR (Predictive Ratio Risk) and R2 have been used to analyse the model. The model fits significantly better than other existing models.

A Sensitivity Analysis of a New NHPP Software Reliability Model with the Generalized Exponential Fault Detection Rate Function Considering the Uncertainty of Operating Environments

Early in the development of the software reliability model, it was assumed that the operating environment of the software was used in the same or similar field environment as the test environment. However, they may also be used in many different locations that may differ from the environment in which they were developed and tested. And also, in general, software systems are known to degrade performance and increase failure rate as their service life increases. Therefore, efforts have been made to expand the exponential distribution assuming a constant failure rate. In this paper, we propose a new software reliability model with the generalized exponential (GE) fault detection rate function considering the uncertainty of operation environments based on a non-homogeneous Poisson process (NHPP). We examine the goodness-of-fit of a new NHPP software reliability model and other existing models based on dataset. As a result of comparison of goodness-of-fit, it can be confirmed that the new proposed model is significantly better by showing that all the criteria of the new proposed model are the lowest compared to other models. In addition, the results for the sensitivity analysis show that the parameters of a new NHPP software reliability model affect the mean value function.

A Software reliability model with a fault detection rate function of the generalized exponential distribution

The software reliability model is modeled on the basis of the number of software failures and the interval time between failures, and predicts how failures will occur in the future by estimating reliability evaluation measures such as failure interval time, software reliability and failure rate, In general, software systems are known to degrade performance and increase failure rate as their service life increases. Therefore, efforts have been made to expand the exponential distribution assuming a constant failure rate. In this paper, we propose a new software reliability model with a fault detection rate function of the generalized exponential distribution based on a non-homogeneous Poisson process (NHPP). We examine the goodness-of-fit of a new NHPP software reliability model and other existing models based on two data sets. The comparative results for the goodness-of-fit show that the proposed model does significantly better than the existing models.

NHPP Software Reliability Model with Inflection Factor of the Fault Detection Rate Considering the Uncertainty of Software Operating Environments and Predictive Analysis

The non-homogeneous Poisson process (NHPP) software has a crucial role in computer systems. Furthermore, the software is used in various environments. It was developed and tested in a controlled environment, while real-world operating environments may be different. Accordingly, the uncertainty of the operating environment must be considered. Moreover, predicting software failures is commonly an important part of study, not only for software developers, but also for companies and research institutes. Software reliability model can measure and predict the number of software failures, software failure intervals, software reliability, and failure rates. In this paper, we propose a new model with an inflection factor of the fault detection rate function, considering the uncertainty of operating environments and analyzing how the predicted value of the proposed new model is different than the other models. We compare the proposed model with several existing NHPP software reliability models using real software failure datasets based on ten criteria. The results show that the proposed new model has significantly better goodness-of-fit and predictability than the other models.

Optimal Release Time and Sensitivity Analysis Using a New NHPP Software Reliability Model with Probability of Fault Removal Subject to Operating Environments

With the latest technological developments, the software industry is at the center of the fourth industrial revolution. In today’s complex and rapidly changing environment, where software applications must be developed quickly and easily, software must be focused on rapidly changing information technology. The basic goal of software engineering is to produce high-quality software at low cost. However, because of the complexity of software systems, software development can be time consuming and expensive. Software reliability models (SRMs) are used to estimate and predict the reliability, number of remaining faults, failure intensity, total and development cost, etc., of software. Additionally, it is very important to decide when, how, and at what cost to release the software to users. In this study, we propose a new nonhomogeneous Poisson process (NHPP) SRM with a fault detection rate function affected by the probability of fault removal on failure subject to operating environments and discuss the optimal release time and software reliability with the new NHPP SRM. The example results show a good fit to the proposed model, and we propose an optimal release time for a given change in the proposed model.

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

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.

A Software Reliability Model with a Weibull Fault Detection Rate Function Subject to Operating Environments

When software systems are introduced, these systems are used in field environments that are the same as or close to those used in the development-testing environments; however, they may also be used in many different locations that may differ from the environment in which they were developed and tested. As such, it is difficult to improve software reliability for a variety of reasons, such as a given environment, or a bug location in code. In this paper, we propose a new software reliability model that takes into account the uncertainty of operating environments. The explicit mean value function solution for the proposed model is presented. Examples are presented to illustrate the goodness of fit of the proposed model and several existing non-homogeneous Poisson process (NHPP) models and confidence intervals of all models 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.

The Optimal Release Time using Software Reliability Model with a Burr Type III Fault Detection Rate Function

The Optimal Release Time using Software Reliability Model with a Burr Type III Fault Detection Rate Function

An imperfect-debugging fault-detection dependent-parameter software

Software reliability growth models (SRGMs) incorporating the imperfect debugging and learning phenomenon of devel- opers have recently been developed by many researchers to estimate software reliability measures such as the number of remaining faults and software reliability. However, the model parameters of both the fault content rate function and fault detection rate function of the SRGMs are often considered to be independent from each other. In practice, this assumption may not be the case and it is worth to investigate what if it is not. In this paper, we aim for such study and propose a software reliability model connecting the imperfect debugging and learning phenomenon by a common parameter among the two functions, called the imperfect-debugging fault-detection dependent-parameter model. Software testing data collected from real applications are utilized to illustrate the proposed model for both the descriptive and predictive power by determining the non-zero initial debugging process.