Software reliability prediction using a deep learning model based on the RNN encoder–decoder

NHPP software reliability model considering the uncertainty of operating environments with imperfect debugging and testing coverage

Modeling and analysis of reliability of multi-release open source software incorporating both fault detection and correction processes

This paper proposes a generalized model to cover imperfect debugging and the uncertainty of the operating environment and its effect on fault detection rate into software reliability evaluation based on a non-homogeneous Poisson process (NHPP). Many NHPP software reliability growth models (SRGMs) have been developed to estimate the software reliability measures over the past 40 years, but most of these models assume that the operating environment is the same as the testing environment. However, in fact, due to the unpredictability of the uncertain factors in the operating environments for the software, they may considerably influence the software’s reliability in an unpredictable way. So when a software system works in a field environment, its reliability is usually different from the original reliability prediction in the testing phase of the software development process, also from all its similar applications in other fields. In this paper, a general model is used to derive models that incorporate the uncertainty of operating environments, which provides the flexibility in considering a different fault detection rate and random environmental factor and so on. Several published models are shown to be covered by this general model and a new model is also developed and examined. The numerical illustrative examples of the proposed model have been validated on two sets of real software failure data in terms of six criteria. The comparison results demonstrate that the new model can fit and predict significantly better than other existing models.

/pdf/a-generalized-software-reliability-growth-model-with-1ybww19aog.pdf

A Generalized Software Reliability Growth Model With Consideration of the Uncertainty of Operating Environments

In this paper, we propose a software reliability model that considers not only error generation but also fault removal efficiency combined with testing coverage information based on a nonhomogeneous Poisson process (NHPP). During the past four decades, many software reliability growth models (SRGMs) based on NHPP have been proposed to estimate the software reliability measures, most of which have the same following agreements: 1) it is a common phenomenon that during the testing phase, the fault detection rate always changes; 2) as a result of imperfect debugging, fault removal has been related to a fault re-introduction rate. But there are few SRGMs in the literature that differentiate between fault detection and fault removal, i.e. they seldom consider the imperfect fault removal efficiency. But in practical software developing process, fault removal efficiency cannot always be perfect, i.e. the failures detected might not be removed completely and the original faults might still exist and new faults might be introduced meanwhile, which is referred to as imperfect debugging phenomenon. In this study, a model aiming to incorporate fault introduction rate, fault removal efficiency and testing coverage into software reliability evaluation is developed, using testing coverage to express the fault detection rate and using fault removal efficiency to consider the fault repair. We compare the performance of the proposed model with several existing NHPP SRGMs using three sets of real failure data based on five criteria. The results exhibit that the model can give a better fitting and predictive performance.

/pdf/a-testing-coverage-software-reliability-model-considering-1e06hl6n2s.pdf

A testing-coverage software reliability model considering fault removal efficiency and error generation

An imperfect software debugging model considering log-logistic distribution fault content function

In the software testing process, the effective development of software reliability models can be quantified to assess the software reliability. Most of the current software reliability models developed are the parametric models, and assuming that the software fault detection process is Markov or non-homogeneous Poisson process (NHPP). However, due to the complexity of the software testing process, assumptions of building the models can not satisfy the actual software testing situation, and those parametric models set up by assumptions can not accurately predict the software failure process. In this case, we propose a non-parametric method of the software reliability prediction based on the triple exponential smoothing. We compare the proposed non-parametric method with the double exponential smoothing and other software reliability model. The experimental results show that using the proposed non-parametric method can effectively and accurately predict the number of software failure. Furthermore, it does not need a lot of historical fault data and calculations, and can be easily made tool used in the actual software test.

A study on software reliability prediction based on triple exponential smoothing method (WIP)

In general, an assumption of perfect debugging is used to build a software reliability model in the software testing process. However, it is not in accordance with the realistic testing situation. In practice, software testing is a complicated process and includes a lot of impact factors, such as testing environment, testing skill and testing tool, etc. Therefore, a new fault can be introduced when the detected fault is removed. It is a well-known imperfect debugging process. When the code size is larger and complex, more faults can be introduced. Furthermore, the cumulative number of new introduced faults shows a nonlinear growth over time. In this paper, we propose a general imperfect software debugging model considering the nonlinear process of fault introduction and use three historical fault data sets to validate our proposed model. The experimental results show our proposed imperfect debugging model has a better fitting and predicting performance compared with other software reliability models. The assumption considering the nonlinear process of fault introduction is in line with fault introduction changing over time in the practical software testing process. Moreover, our proposed model can effectively fitting the historical fault data and accurately predict the software failure behavior in the actual software testing process.

A General Imperfect Software Debugging Model Considering the Nonlinear Process of Fault Introduction

In the software testing process, many factors can affected the failure distribution, such as the testing strategy, running environment and resource allocation, etc. Once these factors are transformed during testing phase, they could result in the failure intensity function increasing or decreasing non-monotonically and abrupt fluctuations at a certain time interval. Especially, in large software systems, detected complex faults and failures are more difficult to isolate, reproduce and remove in comparison of detected simple faults and failures. Therefore, the fault detection rate shows the irregular fluctuation over time in the software testing process. In this paper, we propose an imperfect software debugging model derived from an Ito -type stochastic differential equation (SDE). In the actual testing process, the cumulative number of fault detection can be considered a stochastic event. Thus, we can model fault detection process using SDE. We estimate the parameters of the proposed model using maximum likelihood estimation. We also use five historical fault data sets to test the performance of the proposed model. To show that the proposed model is the best model among all models, we use a new optimal method to select the best model from various software reliability models. Experimental results show that the proposed model has better fitting and predictive performance than non-homogeneous Poisson process models and other perfect software reliability models based on SDE.

An imperfect software debugging model based on stochastic differential equation

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Papers

An imperfect software debugging model considering log-logistic distribution fault content function

A study on software reliability prediction based on triple exponential smoothing method (WIP)

A General Imperfect Software Debugging Model Considering the Nonlinear Process of Fault Introduction

An imperfect software debugging model based on stochastic differential equation