Existing Software Reliability Models Research Articles

Redefining software reliability modeling: embracing fault-dependency, imperfect removal, and maximum fault considerations

Software reliability is a critical aspect of ensuring the quality and dependability of software systems. However, existing software reliability models often make assumptions that do not align with real-world scenarios, such as perfect fault removal and independent faults. In this paper, we address this gap by developing a software reliability model that considers fault-dependent detection, imperfect fault removal, and the maximum number of faults that may present in the system. By accounting for these factors, our proposed model aims to provide a more accurate representation of software reliability. We evaluate the effectiveness of our model by comparing it to existing models using three commonly used goodness-of-fit criteria. The results demonstrate the importance of incorporating these considerations in software reliability modeling and highlight the superiority of our approach in capturing the complexities associated with software faults. Additionally, this paper conducts an analysis of optimal release planning, which yields highly encouraging results for software managers and engineers. This analysis adds significant value to the existing literature, further emphasizing the practical relevance of our proposed model.

Open-source Software Reliability Modeling with Stochastic Impulsive Differential Equations

In reality, sudden updates of software, attacks of hackers, influence of the Internet market, etc. can cause a surge in the number of open-source software (OSS) faults (this moment is the time when impulse occurs), which results in impulsive phenomenon. For the existing software reliability models, dynamic process of software fault is considered to be continuous when assessing reliability, but continuity of the process can be disrupted with appearance of random impulses. Thus, to more accurately assess software reliability, we proposed an OSS reliability model with SIDE. In the model, dynamic process of software fault is divided into a continuous and a skipped part, described the continuous part of the process with SDE, and described destruction of the continuity caused by unpredictable random events with random impulses. Finally, the proposed model is verified with two datasets from real OSS project, and the results show that the proposed model is more in line with reality and has better fitting effect than the existing models.

New failure rate model for iterative software development life cycle process

Software reliability models are one of the most generally used mathematical tool for estimation of reliability, failure rate and number of remaining faults in the software. Existing software reliability models are designed to follow waterfall software development life cycle process. These existing models do not take advantage of iterative software development process. In this paper, a new failure rate model centered on iterative software development life cycle process has been developed. It aims to integrate a new modulation factor for incorporating varying needs in each phase of iterative software development process. It comprises imperfect debugging with the possibility of fault introduction and removal of multiple faults in an interval as iterative development of the software proceeds. The proposed model has been validated on twelve iterations of Eclipse software failure dataset and nine iterations of Java Development toolkit (JDT) software failure dataset. Parameter estimation for the proposed model has been done by hybrid particle swarm optimization and gravitational search algorithm. Experimental results in-terms of goodness-of-fit shows that proposed model has outperformed Jelinski Moranda, Shick Wolverton, Goel Okummotto Imperfect debugging, GS Mahapatra, Modified Shick Wolverton in 83.33% of iterations for eclipse dataset and 77.77% of iterations for JDT dataset.

Using Hybrid Algorithm to Estimate and Predicate Based on Software Reliability Model

The software reliability is mainly obtained through modeling and estimating. The existing software reliability models are nonlinear, and the parameter estimation of these models is difficult. At the same time, there are many optimization methods for solving the nonlinear function problems, such as artificial bee colony algorithm (ABC) and Particle Swarm Optimization (PSO). ABC has the characteristics of fewer control parameters, strong exploration ability, and the high accuracy of the solution. The PSO algorithm has the characteristics of the relatively small amount of computation and fast search speed but it has premature convergence, especially in dealing with complex multi-peak search problems, and the problem of poor local search ability. This paper proposes the parameter estimation method of software reliability model based on hybrid PSO-ABC, constructs a new fitness function based on maximum likelihood estimation, removes the wrong solution during the algorithm execution process, and adds knowledge to improve the solution accuracy. This paper uses five classic sets of software failure data to estimate the GO model parameters and make predictions and performs a variety of comparisons of the algorithm results. The experimental results show that the new fitness function is better, the solution of parameter estimation using the hybrid PSO-ABC is more accurate, and the hybrid PSO-ABC has a great advantage in generality and especially in limited data.

A software reliability model incorporating martingale process with gamma-distributed environmental factors

As the increasing application of software system in various industry, software reliability gains more attention from the researchers and practitioners in the past few decades. The goal of such an expanding application of software system is to continuously bring convenience and functionality in everyday life. Lots of environmental factors defined by many studies may have positive/negative impact on software reliability during the development process (Zhu et al. in J Syst Softw 109:150–160, 2015; Clarke and O’Connor in Inf Softw Technol 54(5):433–447, 2012; Zhu and Pham in J Syst Softw 1–18, 2017b). However, most existing software reliability models have not incorporated these environmental factors in the model consideration. In this paper, we propose a theoretic software reliability model incorporating the fault detection process is a stochastic process due to the randomness caused by the environmental factors. The environmental factor, Percentage of Reused Modules, is described as a gamma distribution in this study based on the collected data from industry. Open Source Software project data are included to demonstrate the effectiveness and predictive power of the proposed model.

Generalized Software Reliability Model Considering Uncertainty and Dynamics: Model and Applications

Today’s development environment has changed drastically; the development periods are shorter than ever and the number of team members has increased. Consequently, controlling the activities and predicting when a development will end are difficult tasks. To adapt to changes, we propose a generalized software reliability model (GSRM) based on a stochastic process to simulate developments, which include uncertainties and dynamics such as unpredictable changes in the requirements and the number of team members. We assess two actual datasets using our formulated equations, which are related to three types of development uncertainties by employing simple approximations in GSRM. The results show that developments can be evaluated quantitatively. Additionally, a comparison of GSRM with existing software reliability models confirms that the approximation by GSRM is more precise than those by existing models.

Software reliability growth models: A comparison of linear and exponential fault content functions for study of imperfect debugging situations

AbstractThe software testing process basically aims at building confidence in the software for its use in real world applications. The reliability of a software system is always important to us. As we carry out the error detection and correction phenomenon on our software, the reliability of the software grows. With an aim to model this growth in the software reliability, many formulations in the form of Software Reliability Growth Models have been proposed. Many of these are based on Non-Homogeneous Poisson Process framework.In this paper, a parallel comparison of the performance of the proposed software reliability growth models is carried out, considering linear and exponential fault content functions for study of imperfect debugging situations. The performance of the proposed models has been compared with some famous existing software reliability models and the proposed models have been validated on some real-world datasets. Three goodness-of-fit criteria that include mean square error, predictive-rat...

A software reliability model with time-dependent fault detection and fault removal

The common assumption for most existingsoftware reliability growth models is that fault is independent and can be removed perfectly upon detection. However, it is often not true due to various factors including software complexity, programmer proficiency, organization hierarchy, etc. In this paper, we develop a software reliability model with considerations of fault-dependent detection, imperfect fault removal and the maximum number of faults software. The genetic algorithm (GA) method is applied to estimate the model parameters. Four goodness-of-fit criteria, such as mean-squared error, predictive-ratio risk, predictive power, and Akaike information criterion, are used to compare the proposed model and several existing software reliability models. Three datasets collected in industries are used to demonstrate the better fit of the proposed model than other existing software reliability models based on the studied criteria.

Classification of Software Reliability Models to Improve the Reliability of Software

Background/Objectives: The main objective is to present a classification of reliability models that would be useful in determining which of the existing model to use in a given software development environment. Methods/Statistical Analysis: In this research importance is given on comparison of existing software reliability models. The analytical models are mostly useful in estimating and monitoring reliability. The models can help software testing/debugging managers to make predictions about the anticipated future reliability of software under growth. Findings: This paper provides a detailed study of existing software reliability models which claim to progress software quality through efficient determination of software faults. Failure behavior of the software as predicted by these models has important implication in understanding the performance of the software and its improvement. One unique part of this paper is that we do not add any new models to the already large collection of models; rather we give importance to the taxonomy of models used in the software development process. Application/Improvements: The models are supportive for the software practitioners to master the schedule of the projects, the performance of the programmers and to improve the reliability of software system.

Assessing Reliable Software using SPRT based on LPETM

Software reliability assessment is increasingly important in developing and testing new software products. Logarithmic Poisson Execution Time Model (LPETM) is a software reliability model which predicts the expected failures and hence related reliability quantities better than existing software reliability models. It uses Non-Homogeneous Poisson Process(NHPP) with a mean value function that is dependent on exponentially falling fault detection rate. The well known sequential Probability Ratio Test(SPRT) procedure of statistical science is adopted for this model in order to decide upon the reliability / unreliability of developed software. The model is evaluated by using 6 Data Sets. General Terms Decision Rule , Software testing, Software failure data,Quality Software. .

Software Reliability Modeling Considering Fault Correction Process

Many existing software reliability models (SRMs) are based on the assumption that fault correction activities take a negligible amount of time and resources, which is often invalid in real-life situations. Consequently, the estimated and predicted software reliability tends to be over-optimistic, which could in turn mislead management in related decision-makings. In this paper, we first make an in-depth analysis of real-life software testing process; then a Markovian SRM considering fault correction process is proposed. Parameter estimation method and software reliability prediction method are established. A numerical example is given which shows that by using the proposed model and methods, the results obtained tend to be more appropriate and realistic.

A mixture and self-exciting model for software reliability

This paper develops a model, based on both properties of mixture and self-exciting, that generalizes existing software reliability models. We also offer a model classification method. Details are proven with some specific cases and examples illustrating the results are given.

Software failure prediction based on a Markov Bayesian network model

Due to the complexity of software products and development processes, software reliability models need to possess the ability of dealing with multiple parameters. Also in order to adapt to the continually refreshed data, they should provide flexibility in model construction in terms of information updating. Existing software reliability models are not flexible in this context. The main reason for this is that there are many static assumptions associated with the models. Bayesian network is a powerful tool for solving this problem, as it exhibits strong ability to adapt in problems involving complex variant factors. In this paper, a software prediction model based on Markov Bayesian networks is developed, and a method to solve the network model is proposed. The use of our model is illustrated with an example.

Markovian software reliability measurement with a geometrically decreasing perfect debugging rate

The assumption of perfect debugging is a controversial issue in software reliability modeling. Most existing software reliability models assume that all faults causing software failures are detectable and correctable, and that no new faults are introduced into the software system by debugging activities. This paper discusses a software reliability model for an imperfect debugging environment where the detected faults are not always corrected and removed from the system. Taking notice of the cumulative number of corrected faults, we relate the uncertainty of debugging activities to the increase in complexity of corrected faults. The stochastic behavior of the fault-correction phenomenon with imperfect debugging is described by a Markov process. Several quantitative measures for software reliability assessment are derived from this model.

Bayesian Software Reliability Models Based on Martingale Processes

This article proposes new Bayesian models for software reliability based on a piecewise constant failure rate. A martingale process prior is assumed on the failure rate. Three different hyperprior models on the martingale process are considered. The first two models assume that the conditional variance is dependent on the mean, whereas the third model assumes constant conditional variance. Markov chain sampling–based posterior analysis and prequential predictions are developed for these models and are illustrated in a software failure dataset. In addition, model comparison is studied via the Bayes factor criterion. General techniques are described for estimating the marginal likelihood of the proposed models as well as of many existing software reliability models, and these are illustrated in two datasets.

Comparisons of nonhomogeneous Poisson process software reliability models and its applications

In this paper, existing software reliability models based on an nonhomogeneous Poisson process (NHPP) are summarized. A recently developed NHPP model with imperfect debugging and time-dependent fault detection rate is examined. The results show that this proposed model provides improved descriptive and predictive power when compared with other existing NHPP models. The model can also be incorporated into a software cost model for reliability assessment. The optimal release policies which minimize the expected total software cost is also presented.

Censored software-reliability models

Nonfailure stops of software execution processes can be viewed as a type of censored data. They can occur in a wide range of computing systems (e.g., concurrent computing systems, data sampling systems, transaction processing systems) due to technical or nontechnical reasons. Using existing software reliability models to deal with this type of censored software reliability data, viz, successive inter-stop times, where a stop can be failure or nonfailure stop, means that nonfailure stops are disregarded. This paper develops censored software reliability models, or censored forms of existing software reliability models, to account for nonfailure stops and directly deal with censored data of software reliability. The paper shows how to develop censored forms for the models: Jelinski-Moranda, Schick-Wolverton, Moranda Geometric, and Littlewood-Verrall, and discusses the corresponding validation forms. Censored forms of other software reliability models can be developed in a similar way. Censored software reliability models reduce to noncensored software reliability models if no nonfailure stop occurs.

A software reliability growth model

Most of the existing software reliability models assume time between failures to follow an exponential distribution. Develops a reliability growth model based on non‐homogeneous Poisson process with intensity function given by the power law, to predict the reliability of a software. Several authors have suggested the use of the non‐homogeneous Poisson process to assess the reliability growth of software and to predict their failure behaviour. Inference procedures considered by these authors have been Bayesian in nature. Uses an unbiased estimate of the failure rate for prediction. Compares the performance of this model with Bayes empirical‐Bayes models and a time series model. The model developed is realistic, easy to use, and gives a better prediction of reliability of a software.

Software Reliability Models for Computer Implementations — An Empirical Study

In this paper we report on an empirical study conducted to determine whether existing software reliability models can be used to predict the errors incurred during the design, development and testing phases of the microcode of computer systems. A number of reliability models are reviewed and employed to predict the faults of the microcode development of the IBM 4381 and the IBM 9370 families of computer systems. Through the study of historical defect data from these two systems, we establish an empirical basis for the usefulness of such models in terms of accuracy in the prediction of the expected errors during the development of the microcode of computer systems.

A Bayesian analysis of the logarithmic-Poisson execution time model based on expert opinion and failure data

We propose a Bayesian approach for predicting the number of failures in a piece of software, using the logarithmic-Poisson model, a nonhomogeneous Poisson process (NHPP) commonly used for describing software failures. A similar approach can be applied to other forms of the NHPP. The key feature of the approach is that now we are able to use, in a formal manner, expert knowledge on software testing, as for example, published information on the empirical experiences of other researchers. This is accomplished by treating such information as expert opinion in the construction of a likelihood function which leads us to a joint distribution. The procedure is computationally intensive, but for the case of the logarithmic-Poisson model has been codified for use on a personal computer. We illustrate the working of the approach via some real live data on software testing. The aim is not to propose another model for software reliability assessment. Rather, we present a methodology that can be invoked with existing software reliability models.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>