Optimizing reliability and release time of testing effort dependent module-based SRGM with multi-fault severity integration

Reliability is the one of the important quality measure of software product. Testing is one important phase in software development life cycle whose intention is to find the bugs present in the software product. A mathematical model which describes the software testing in software development cycle termed as software reliability growth models. Past few decades many software reliability growth models were proposed. Estimating the accurate release time of software is an important challenging issue of software Development Company. If the software product is released early contain more errors makes software to be less reliable, where as late release will increase the development cost. It is observed that more than half of the total development cost is concentrated during the maintenance phase. Estimating the reliability and accurate development cost of software product by software reliability growth model at maintenance phase is an important issue. Warranty is defined to be an agreement between the customer and vendor of the software to provide the extra protection to the product. Several papers have concentrated on optimal release time by considering the warranty cost. All early proposed NHPP warranty cost models considered a perfect-debugging software reliability growth model while estimating the software maintenance cost. But it was observed that, the software product is influenced by several factors like environment, resources and nature of the faults during the operational phase. In such circumstances it is quite not right to consider a perfect NHPP software reliability growth model in operational phase. In this paper we proposed an imperfect debugging software reliability growth model by combining the cost and reliability at a given warranty period to estimate the optimal release time of software product.

Failure of a safety critical system can lead to big losses. Very high software reliability is required for automating the working of systems such as aircraft controller and nuclear reactor controller software systems. Fault-tolerant softwares are used to increase the overall reliability of software systems. Fault tolerance is achieved using the fault-tolerant schemes such as fault recovery (recovery block scheme), fault masking (N-version programming (NVP)) or a combination of both (Hybrid scheme). These softwares incorporate the ability of system survival even on a failure. Many researchers in the field of software engineering have done excellent work to study the reliability of fault-tolerant systems. Most of them consider the stable system reliability. Few attempts have been made in reliability modeling to study the reliability growth for an NVP system. Recently, a model was proposed to analyze the reliability growth of an NVP system incorporating the effect of fault removal efficiency. In this model, a proportion of the number of failures is assumed to be a measure of fault generation while an appropriate measure of fault generation should be the proportion of faults removed. In this paper, we first propose a testing efficiency model incorporating the effect of imperfect fault debugging and error generation. Using this model, a software reliability growth model (SRGM) is developed to model the reliability growth of an NVP system. The proposed model is useful for practical applications and can provide the measures of debugging effectiveness and additional workload or skilled professional required. It is very important for a developer to determine the optimal release time of the software to improve its performance in terms of competition and cost. In this paper, we also formulate the optimal software release time problem for a 3VP system under fuzzy environment and discuss a the fuzzy optimization technique for solving the problem with a numerical illustration.

Many software reliability growth models have been proposed in the past decade. Those models tacitly assume that testing-effort expenditures are constant throughout software testing. This paper develops realistic software reliability growth models incorporating the effect of testing-effort. The software error detection phenomenon in software testing is modeled by a nonhomogeneous Poisson process. The software reliability assessment measures and the estimation methods of parameters are investigated. Testing-effort expenditures are described by exponential and Rayleigh curves. Least-squares estimators and maximum likelihood estimators are used for the reliability growth parameters. The software reliability data analyses use actual data. The software reliability growth models with testing-effort can consider the relationship between the software reliability growth and the effect of testing-effort. Thus, the proposed models will enable us to evaluate software reliability more realistically.

Software reliability assessment for an exponential-S-shaped reliability growth phenomenon

Significant advances in the field of software testing have been made by providing extended warranties, operational support and desired software reliability to the customers. However, the trade-off between minimising testing cost and maximising reliability to ensure well-timed market penetration has been a key concern for developers. Separate release and testing stop time strategy in order to optimise overall testing and market opportunity cost is proposed. We propose a generalised framework for developers to achieve multiple objectives of minimum testing and market opportunity cost; optimal warranty length; and optimal testing and release time. Software reliability growth models (SRGMs) are used to model an average number of bugs detected by testers (users) during the pre (post) release of software. Significant implications and quite intriguing facts related to separate release and testing stop time; early release ensuring higher reliability and; capitalising market opportunity to minimise overall testing cost are suggested based on proposed optimisation models. Numerical illustration is provided to validate the proposed framework.

The safety-critical software of Reactor Protection System (RPS) plays a significant role for the safe operation of the nuclear power plant (NPP). However, it also brings challenges both to the reliability analysis of the RPS and to the Probabilistic Risk Assessment of the NPPs. The reliability analysis of safety-critical software is also expected by the nuclear regulation agencies and the software development groups for test evaluation and optimization. The detected faults during the software test process are regarded to have close connection with the software reliability and there have been hundreds of test-based software reliability models. Due to the particularity of its function, the safety-critical software of an NPP is especially sensitive to the faults with higher severity levels which should be paid special attention to. Severity levels of faults are commonly taken into account during software reliability modelling. In this paper, a novel software reliability growth model considering actual severity data was built based on a non-homogeneous Poisson process. Ratio of fatal faults with highest severity level to all accumulated faults was modelled with logistic curve. Then the mean value functions of both fatal and general faults were derived. A belief factor was employed to describe the trend of severity classification. In the end, the test data of a practical project was used to verify this model. The analysis results showed that this model had better prediction effect than Goel-Okumoto model and Inflection S-shaped model even when limited test data were collected. This novel model can be used as a helpful tool to evaluate both the reliability and the release time of the safety-critical software of an NPP.

In reality, the fault detection (or correction) phenomenon and software reliability estimation in the operational phase are different from that in the testing phase. The fault removal continues at a slower rate during the operational phase. In this paper, we will investigate some techniques for software reliability prediction and measurement in the operational phase. We first review how some software reliability growth models (SRGMs) based on non-homogeneous Poisson processes (NHPPs) can be readily derived based on a unified theory for NHPP models. Under this general framework, we can not only verify some conventional SRGMs but also derive some new SRGMs that can be used for software reliability measurement in the operational phase. That is, based on the unified theory, we can incorporate the concept of multiple changepoints into software reliability modeling. We can formularize and simulate the fault detection process in the operational phase. Some numerical illustrations based on real software failure data are also presented. The experimental results show that the proposed models can easily reflect the possible changes of fault detection process and offer quantitative analysis on software failure behavior in field operation.

In this paper, we propose a new software reliability growth model which is the mixture of two exponential reliability growth models, one of which has the reliability growth and the other one does not have the reliability growth after the software is released upon completion of testing phase. The mixture of two such models is characterized by a weighted factor p, which is the proportion of reliability growth part within the model. Firstly, this paper discusses an optimal software release problem with regard to the expected total software cost incurred during the warranty period under the proposed software reliability growth model, which generalizes Kimura, Toyota and Yamada's (1999) model with consideration of the weighted factor. The second main purpose of this paper is to apply the Bayesian approach to the optimal software release policy by assuming the prior distributions for the unknown parameters contained in the proposed software reliability growth model. Some numerical examples are presented for the purpose of comparing the optimal software release policies depending on the choice of parameters by the non-Bayesian and Bayesian methods.

Multiple Software Reliability Growth Models (SRGM) has been illustrated to inspire designers and software developers to develop quality software. These models implicitly assume that expenditure is constant throughout testing effort. Recently importance was given to the testing phase by the development team and it requires controlled screening closely to raise its overall operational efficiency. Software Reliability Growth Models exhibits a precise relation between the failure patterns with time. Considering this as a basic building block a SRGM which is a Non-Homogeneous Poisson Process in nature is i in this work. The various aspects of our proposed model are testing effort function and fault detection rate (FDR). Mainly in Software Reliability Growth Model it was assumed that fault removal process are perfect which is not true in reality as new faults may get introduced during removal or some faults may not get removed due to its complexity. Thus in this model the concept of imperfect debugging was considered and assists us to justify the software reliability more convincingly through the help of testing effort function.

It is one of the important issues in modern software development to make a highly reliable software system by efficient and economical testing. As one of these solutions, many companies have taken in testing-progress management and quality/reliability assessment based on software reliability growth models. Consequently, many software reliability growth models reflecting various development factors and operational environment of the software system have been proposed. Most of these models have assumed the software system as one domain. That is, the models have assumed that the set of the testing-paths influenced by execution of test-cases is extended with testing-progress and it expands to whole software system finally. However, a software system has the structure that the main-component calls plural sub-components ordinarily. Therefore, unless the main-component is tested, the sub-component called from it is not tested. In this chapter, we propose software reliability growth models based on such components' characteristics. Especially, these models reflecting the different testing-environment for composed components are formulated by nonhomogeneous Poisson processes. Furthermore, using fault-detection data observed in actual development projects, we show numerical examples of software reliability assessment and results of goodness-of-fit comparisons with existing software reliability growth models.

A large number of software reliability growth models (SRGMs) have been proposed during the past 30 years to estimate software reliability measures such as the number of remaining faults, software failure rate, and software reliability. Selection of an optimal SRGM for use in a particular case has been an area of interest for researchers in the field of software reliability. Tools and techniques for software reliability model selection found in the literature cannot be used with high confidence as they use a limited number of model selection criteria. For the first time, we developed a deterministic quantitative model based on a distance based approach (DBA) method, then applied it for evaluation, optimal selection, and ranking of SRGMs. DBA recognizes the need for relative importance of criteria for a given application, without which inter-criterion comparison could not be accomplished. It requires a set of model selection criteria, along with a set of SRGMs, and their level of criteria for optimal selection; and it successfully presents the results in terms of a merit value which is used to rank the SRGMs. We use two distinct, real data sets for demonstration of the DBA method. The result of this study will be a ranking of SRGMs based on the Euclidean composite distance of each alternative to the designated optimal SRGM.

The 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-ratio risk and predictive power are used to carry out the performance comparison of the models. Using these comparison criteria on six actual failure datasets, it is concluded that the proposed Model-2 which always outperforms Model-1, fits the actual failure data better and has better predictive power than other considered SRGMs for at least two data sets.

PurposeThe aim of this paper is to propose a software reliability growth model based on stochastic differential equations for the integration testing phase of distributed development environment.Design/methodology/approachA client/server system (CSS), which is a new development method, has come into existence as a result of the progress of networking technology by UNIX systems. On the other hand, the effective testing method for distributed development environment has only a few presented. The method of software reliability assessment considering the interaction among software components in a distributed one is discussed.FindingsConventional software reliability growth models for system testing phase in distributed development environment have included many unknown parameters. Especially, the effective estimation method in terms of these unknown parameters, which means the proportion of the total testing‐load for the software component, has never been presented. This software reliability growth model can be easily applied in distributed software development, because the model has a simple structure.Practical implicationsThis model is very useful for software developers in terms of practical reliability assessment in the actual distributed development environment.Originality/valueThe method of software reliability assessment considering the interaction among software components in distributed development environment is proposed. Additionally, several numerical examples for the actual data are presented.

A software release problem based on four software reliability growth models (SRGMs) with random life-cycle length is studied. Test of the software system is terminated after time T and released (sold) to the user at a price. The price of the software system and three cost components are considered, and average total profit is used as a criterion. The optimal values of release times are shown to be finite and unique. Hence, the optimal solutions can be obtained numerically by, for example, a bisection method. A numerical example indicates that the optimal release time increases as (1) the error rate in each model decreases and (2) the difference between the error fixing cost during the test phase and that during the operational phase increases. The case of unknown model parameters is considered only for the Jelinski-Moranda model because a Bayes model is not available for other SRGMs. The release decision depends on testing time, but other stopping rules, for example based on the number of corrected errors, can be considered.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Quality, cost, and delivery are important software development management factors that software development managers control during the testing phase of the software development process. In particular, the software development management techniques based on software reliability growth models (SRGM) are often used. The majority of SRGMs are developed by treating the software failure-occurrence time or the fault-detection time intervals as random variables, and they assume that the stochastic characteristics of these quantities are the same throughout the testing phase. However, researchers often observe that stochastic characteristics of software failure-occurrence time intervals change because of a change in the testing environment. The chapter discusses the optimal software release problem based on the change-point model as an application. It describes the optimal software release problem based on the multi-attribute utility theory (MAUT). The MAUT is a utility theory on decision-making considering multiple constraints.