Software reliability prediction and analysis during operational use

Over the past 30 years, many software reliability growth models (SRGMs) have been proposed for estimation of reliability growth of software. In fact, effective debugging will not be easy because the fault may not be immediately obvious. In the past, some researchers ever used an infinite server queueing (ISQ) model to describe the software debugging behavior. An infinite-server queueing model is considered where access of customers to service is controlled by a gate and the gate is open only if all servers are free. However, the finite server queueing (FSQ) model is first advantageously modeled as an infinite-server system. Thus, in this paper, we will show how to incorporate both FSQ and ISQ models into software reliability estimation and prediction. In addition, here we also consider the factor of perfect/imperfect debugging. Experimental results show that the proposed framework to incorporate both fault detection and correction processes for SRGM has a fairly accurate prediction capability.

In this paper we consider non-parametric estimation methods for software reliability assessment without specifying the fault distribution, where the underlying stochastic process to describe software fault-counts in the system testing is given by a non-homogeneous Poisson process. A comprehensive approach based on the kernel estimation is provided with several kernel functions and bandwidth estimations. Next, we develop interval estimation methods via the non-parametric bootstrap, and derive the confidence regions of several reliability measures such as the expected cumulative number of software faults, software intensity function, quantitative software reliability as well. The resulting data-driven methodology can give the useful probabilistic information on the software reliability prediction under the incomplete knowledge on fault distribution. In illustrative examples with a real software fault data, it is shown that the proposed methods provide useful software reliability measures under uncertainty from the view point of frequentist analysis.

Over the past three decades, many software reliability growth models (SRGMs) were proposed and they are aimed at predicting and estimating software reliability. One common assumption of these conventional SRGMs is that detected faults will be removed immediately. In reality, this assumption may not be reasonable and may not always occur. Developers need time to identify the root causes of detected faults and then fix them. Besides, during debugging the fault correction rate may not be a constant and could be changed at some certain points as time proceeds. Consequently, in this paper, we will explore and study how to apply queueing model to investigate the fault correction process during software development. We propose an extended infinite server queueing model with multiple change-points to predict and assess software reliability. Experimental results based on real failure data show that proposed model can depicts the change of fault correction rates and predict the behavior of software development more accurately than traditional SRGMs.

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.

Management technologies for improving software reliability are very important for software TQM (Total Quality Management). The quality characteristics of software reliability is that computer systems can continue to operate regularly without the occurrence of failures on software systems. In this chapter, we describe several recent developments in software reliability modeling and its applications as quantitative techniques for software quality/reliability measurement and assessment. That is, a quality engineering analysis of human factors affecting software reliability during the design-review phase, which is the upper stream of software development, and software reliability growth models based on stochastic differential equations and discrete calculus during the testing phase, which is the lower one, are discussed. And, we discuss quality-oriented software management analysis by applying the multivariate analysis method and the existing software reliability growth models to actual process monitoring data. Finally, we investigate an operational performability evaluation model for the software-based system, introducing the concept of systemability which is defined as the reliability characteristic subject to the uncertainty of the field environment.KeywordsHuman factor analysisDesign-review experimentOSS reliabilityStochastic differential equationDiscrete modelingDifference equationSoftware managementSoftware project assessmentSoftware performability modelingSystemability assessment

Performance analysis of software reliability growth models with testing-effort and change-point

This paper discusses software reliability growth modeling with effect of changing the software failure-occurrence phenomenon in the testing phase, and checks the software reliability measurement accuracy of our models by using actual data. Especially, our modeling approach in this paper uses a testing-environmental function for describing a relationship between the software failure-occurrence time intervals before the change and those after the change. Further, we discuss a problem for estimating the optimal changing-time and software shipping-time simultaneously from the point of view of the total expected software cost expending in the testing and operational phases. Finally, we show examples of the application of software reliability measurement based on our model and those of the derived optimal policy by using actual data.

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 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.

In order to overcome the limitations of debugging process, insufficient consideration of imperfect debugging and testing-effort (TE) in software reliability modeling and analysis, a software reliability growth model (SRGM) explicitly incorporating imperfect debugging and TE is developed. From the point of view of incomplete debugging and introduction of new fault, software testing process is described and a relatively unified SRGM framework is presented considering TE. The proposed framework models are fairly general models that cover a variety of the previous works on SRGM with ID and TE. Furthermore, a special SRGM incorporating an improved Logistic testing-effort function (TEF) into imperfect debugging modeling is proposed. The effectiveness and reasonableness of the proposed model are verified by published failure data set. The proposed model closer to real software testing has better descriptive and predictive power than other models

Software Reliability Growth Models (SRGMs) provide techniques to predict future failure behavior from known characteristics of the software testing work. However, in some cases, software developers did not have sufficient historical data to estimate the corresponding reliability and the expected testing cost, especially for a newly developed software project, and thus the results obtained from analytical models may not be reliable. In such situations, Bayesian analysis is a reasonable approach to additionally take expert's opinions into account for better decision making. In this paper, we utilized Yamada Delayed S-shaped Model with Bayesian analysis in predicting software reliability and expected testing costs to determine an optimal release time for software systems. Besides, the failure process of software are assumed to be drawn from a non-homogeneous Poisson process (NHPP), and the parameters of the proposed model are assumed to be mutually independent and Gamma distributed. Finally, a numerical example is given to verify the effectiveness of the proposed approach, and the sensitive and risk analyses are performed in light of the numerical example.

This paper discusses two-dimensional software reliability measurement technologies, which describe a software reliability growth process depending on two-types of software reliability growth factors: Testing-time and testing-effort factors. From the view point of actual software failure-occurrence mechanism, it is natural to consider that a software reliability growth process depends on not only testing-time factor but also other software reliability growth factors, i.e., testing-effort factors, such as test-execution time, testing-skill of test engineers, testing-coverage. From such reason, the two-dimensional software reliability measurement technologies enable us to conduct more feasible software reliability assessment than the one-dimensional (conventional) software reliability measurement approach, in which it is assumed that the software reliability growth process depends only on testing-time. We discuss two-types of two-dimensional software reliability growth modeling approaches for feasible software reliability assessment, and conduct goodness-of-fit comparisons of our models with existing one-dimensional software reliability growth models. Finally, as one of the examples for two-dimensional software reliability analysis, we show examples of the application of our two-dimensional software reliability growth model by using actual data.

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.

In the development of techniques for software reliability measurement and prediction, many software reliability growth models have been proposed. Application of these models to real data sources has shown that there is commonly great disagreement in predictions, while none of them has been shown to be more trustworthy than others in terms of predictive quality in all applications. Recent work has largely overcome this problem through the development of specialized techniques which analyse the accuracy of predictions from reliability models. Such techniques allow the user to choose, for future predictions for a particular data source, those models which gave the best predictions in the past, for this data.

One of the established trends of research areas and practices in software engineering that dealt with the measurement and enhancement of reliability is software reliability engineering. Stochastic software reliability models find typical usage for analysis. These are the models that perform modeling on failure process of the software and exploit other software metrics or failure data as cornerstone for parameter estimation. The ability of the models for estimating and predicting the current reliability and future failure behavior, respectively, is high. Due to any failures and faults in the system, a product becomes unreliable. The lack of understanding of nature of the software makes the measurement of software reliability as a challenging task. It is not possible to determine a best way to measure the reliability and other aspects of software. This paper proposes an efficient software reliability growth model (SRGM) in which logistic exponential TEF is exploited. This model offers increased failure rate recognition and suitable ways to resolve faults and so on. Our work estimates the SRGM parameters using optimization algorithm. Such estimation can aid in developing precise software reliability model. In order to accomplish the optimization, we use artificial bee colony (ABC) algorithm. As the parameters optimization considerably improves the quality of parameters to be used for reliability growth model, reliability growth can also be improved considerably.