A study of software reliability on big data open source software

In the software industry, two software engineering development best practices coexist: open-source and closed-source software. The former has a shared code that anyone can contribute, whereas the latter has a proprietary code that only the owner can access. Software reliability is crucial in the industry when a new product or update is released. Applying meta-heuristic optimization algorithms for closed-source software reliability prediction has produced significant and accurate results. Now, open-source software dominates the landscape of cloud-based systems. Therefore, providing results on open- source software reliability - as a quality indicator - would greatly help solve the open-source software reliability growth- modelling problem. The reliability is predicted by estimating the parameters of the software reliability models. As software reliability models are inherently nonlinear, traditional approaches make estimating the appropriate parameters difficult and ineffective. Consequently, software reliability models necessitate a high- quality parameter estimation technique. These objectives dictate the exploration of potential applications of meta- heuristic swarm intelligence optimization algorithms for optimizing the parameter estimation of nonhomogeneous Poisson process-based open-source software reliability modelling. The optimization algorithms are firefly, social spider, artificial bee colony, grey wolf, particle swarm, moth flame, and whale. The applicability and performance evaluation of the optimization modelling approach is demonstrated through two real open-source software reliability datasets. The results are promising

Software has become a vital factor in the fourth industrial revolution. Owing to the increase in demand for software products in various fields (big data, artificial intelligence, the Internet of Things, etc.), the software industry has expanded more than ever before. Therefore, software reliability has become very important, and efforts are being made to increase it. One of these efforts is the development of software reliability models (SRMs). SRMs have been studied for a long time as a model that predicts software reliability by using the number of software faults. Software failures can occur for several reasons, including independent software faults such as code errors and software hangs, as well as dependent cases where code errors lead to other software faults. Recently, due to the diversity of software operating environments, software faults are more likely to occur in a dependent manner, and, for this reason, they are likely to increase rapidly from the beginning and progress slowly to the maximum number thereafter. In addition, many large companies have focused on open-source software (OSS) development, and OSS is being developed by many users. In this study, we propose a new SRM that considers the number of finite faults and dependent faults, and examine the goodness-of-fit of a new SRM and other existing non-homogeneous Poisson process models based on the OSS datasets. Through numerical examples, the proposed model demonstrated a significantly better goodness-of-fit when compared to other existing models, and it also exhibited better results on the newly proposed integrated criteria.

Non-homogeneous Poisson process (NHPP) model with typical reliability growth patterns is an important technology about evaluating software reliability. Two parameters which affect software reliability are original failures number and failure-detected rate. The paper firstly defines the software failure distributed and discusses a few kinds' software reliability models. Non-homogeneous Poisson process model is presented. The models with different parameters are discussed and analyzed. The model's restrict condition and some of parameters, original failures number and failure-detected ratio, are extrapolated. Assessment methodology about key parameters is given.

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 an artificial neural- network-based approach for software reliability estimation and modeling. We first explain the network networks from the mathematical viewpoints of software reliability modeling. That is, we will show how to apply neural network to predict software reliability by designing different elements of neural networks. Furthermore, we will use the neural network approach to build a dynamic weighted combinational model. The applicability of proposed model is demonstrated through four real software failure data sets. From experimental results, we can see that the proposed model significantly outperforms the traditional software reliability models.

Software reliability growth models based on a nonhomogeneous Poisson process (NHPP) have been considered as one of the most effective among various models since they integrate the information regarding testing and debugging activities observed in the testing phase into the software reliability model. Although most of the existing NHPP models have progressed successfully in their estimation/prediction accuracies by modifying the assumptions with regard to the testing process, these models were developed based on the instantaneous fault-removal assumption. In this paper, we develop a generalized NHPP software reliability model considering quasi-renewal time-delay fault removal. The quasi-renewal process is employed to estimate the time delay due to identifying and prioritizing the detected faults before actual code change in the software reliability assessment. Model formulation based on the quasi-renewal time-delay assumption is provided, and the generalized mean value function (MVF) for the proposed model is derived by using the method of steps. The general solution of the MVFs for the proposed model is also obtained for some specific existing models. The numerical examples, based on a software failure data set, show that the consideration of quasi-renewal time-delay fault-removal assumption improves the descriptive properties of the model, which means that the length of time delay is getting decreased since testers and programmers adapt themselves to the working environment as testing and debugging activities are in progress.

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.

Software reliability modelling (SRM) is a mathematics technique to estimate some measures of computer system that relate to software reliability. One group of existing models is using non-homogeneous Poisson process (NHPP) whose fault-number and failure-rate are constant or time-dependent functions. A few studies have been manipulated S-shaped curve to construct their models. However, those works remain some limitations. In this study, we introduce a new model that is based on a generalised S-shaped curve and evaluate it by real data set. After installing it in real code of Matlab and using MLE method to estimate parameter with a range of initial solution, we prove that our model converge to the most basic model of NHPP group, Goel-Okumoto model.

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

Open-source software is gaining popularity in industrial projects due to its accessibility and cost-effectiveness. However, concerns persist about its quality and reliability. To assess software reliability quantitatively, software reliability models are utilized, with the unknown parameters of these models typically determined using statistical techniques. In many cases, these methods fail to converge to the global optimal solution of parameter estimation of nonlinear mathematical models and are quite sensitive to the initial guesses of unknown parameters. This necessitates employing a high-quality parameter estimation technique. The study demonstrates the potential application of nine nature-inspired swarm intelligence-based algorithms to address nonlinear parameter estimation problems and effectively identify the global optimal solution with high likelihood, irrespective of the initial guess. These typical algorithms are classified into several categories, including animal-inspired algorithms such as grey wolf optimizer, insect-inspired algorithms such as artificial bee colony, social spider optimization, firefly algorithm, and moth flame optimization, bird-inspired algorithms such as particle swarm optimization, sea creature-inspired algorithms such as whale optimization algorithm, and plant-inspired algorithms such as flower pollination algorithm and dandelion optimizer. Three real-world, open-source reliability datasets are utilized to assess the efficacy of these algorithms in estimating the parameters of two prominent non-homogeneous Poisson process models in software reliability.

Software reliability models are used to estimate and predict the reliability, number of remaining faults, failure intensity, total software development cost, etc., of a software. Testing coverage is very important for both software developers and customers of software products. Testing converge is a measure that enables software developers to evaluate the quality of tested software and determine how much additional effort is needed to improve the reliability of the software. This paper proposes a software reliability growth model based on a non-homogeneous Poisson process (NHPP) that incorporates a logistic–exponential testing coverage function with imperfect debugging. The proposed model relates the test coverage to fault detection phenomena in debugging. Goodness-of-fit test of the proposed model is conducted using different criteria for two sets of software failure data. The proposed model is compared with other existing NHPP models. A software cost model incorporating testing coverage and an optimal release policy based on the number of remaining faults are developed.

Software is widely used in various fields. There is no place where it is not used from the smallest part to the entire part. In particular, the tendency to rely on software is accelerating as the fields of artificial intelligence and big data become more widespread. Therefore, it is extremely important to evaluate the reliability of software because of the extensive damage that could occur if the software fails. Previously, software reliability models were developed based on mathematical and statistical grounds; however, immediate response was difficult. Therefore, in this study, a software reliability model was developed that depends on data using deep learning, and it was analyzed by replacing the activation function previously used in deep learning with the proposed software reliability model. Since the sigmoid function has a similar shape to the software reliability model, we utilized this to propose a deep learning software reliability model that replaces the activation function, the sigmoid function, with the software reliability function. Two datasets were compared and analyzed using 10 criteria, and the superiority of the proposed deep-learning software reliability model was proved. In addition, the results were compared by changing the parameters utilized in the proposed deep-learning software reliability model by −10%, −5%, 5%, and 10%, and it was found that the larger the parameters, the smaller the change.

The importance of software has increased significantly over time, and software failures have become a critical concern. As software systems have diversified in functionality, their structure has become more complex, and the types of failures that can occur in software have also diversified, stimulating the development of diverse software reliability models. In this study, we make certain assumptions regarding complex software, thereby proposing a new type of non-homogeneous Poisson process (NHPP) software reliability model that considers both dependent cases of software failure and cases that originate from the differences between the developed and actual operating environments. In addition, a new multi-criteria decision method (MCDM) that uses the maximum value for a comprehensive evaluation was proposed to demonstrate the effectiveness of the developed model. This improves the judgment of model excellence through multiple criteria and is suitable for multiple interpretations. To demonstrate the effectiveness of the proposed model, 15 NHPP software reliability models were compared using 13 evaluation criteria and three MCDM methods across two datasets. The results showed that one dataset performed well for all the criteria, whereas the other dataset performed well for the newly proposed a multi-criteria decision method using maximum (MCDMM). The sensitivity analysis also showed a change in the mean value function with a change in the parameters. These results demonstrate that an extended structure for complex software can lead to improved software reliability.

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.

Software development based on the Open Source Software (OSS) model is being increasingly accepted to stand up servers and applications. In particular, Cloud OSS is now attracting attention as the next generation of software products due to cost efficiencies and quick delivery. This chapter focuses on the software reliability modeling and assessment for Cloud computing infrastructure software, especially open source software, such as OpenStack and Eucalyptus. In this chapter, the authors introduce a new approach to the Jump diffusion process based on stochastic differential equations in order to consider the interesting aspect of the numbers of components and users in the reliability model. In addition, the authors consider the network traffic of the Cloud in the reliability modeling and integrate the reliability model with a threshold-based neural network approach that estimates network traffic. Actual software fault-count data are analyzed in order to show numerical examples of software reliability assessment. This chapter also illustrates how the proposed method of reliability analysis can assist in quality improvement in Cloud computing software.