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"software reliability models"
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Deep-Learning Software Reliability Model Using SRGM as Activation Function
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.
Journal Article
Bivariate change-point modeling for software reliability assessment with uncertainty of testing-environment factor
We observe a phenomenon that the probabilistic characteristic of software failure-occurrence time-interval change notably in an actual testing-phase of a software development process. Testing-time observing such phenomenon is ordinarily called change-point. This phenomenon is treated as one of the factors affecting the accuracy of software reliability assessment based on a software reliability growth model. And regarding software reliability growth modeling, a bivariate software reliability growth model, which describes a software reliability growth process depending on the software reliability growth factor consists of testing-time and testing-effort factors, has been proposed for improving software reliability assessment accuracy based on the software reliability growth process. In this paper, we develop a bivariate software reliability growth model considering with the uncertainty of the change of software failure-occurrence phenomenon at the change-point for improving more the accuracy. Finally we show numerical examples of our model by using actual data.
Journal Article
A method for predicting open source software residual defects
Nowadays many commercial projects use open source applications or components (OSS). A recurring problem is therefore the selection of the most appropriate OSS for a project. A relevant criterion for selection is the reliability of the OSS. In this paper, we propose a method that selects the software reliability growth model (SRGM), which among several alternative models best predicts the reliability of the OSS, in terms of residual defects. Several methods exist for predicting residual defects in software, and a widely used method is SRGM. SRGM has underlying assumptions, which are often violated in practice, but empirical evidence has shown that many models are quite robust despite these assumption violations. However, within the SRGM family, many models are available, and it is often difficult to know which models are better to apply in a given context. We present an empirical method that applies various SRGMs iteratively on OSS defect data and selects the model which best predicts the residual defects of the OSS. We empirically validate the method by applying it to defect data collected from 21 different releases of 7 OSS projects. The results show that the method helps in selecting the best model among several alternative models. The method selects the best model 17 times out of 21. In the remaining 4, it selects the second best model.
Journal Article
Efficiency Evaluation of Software Faults Correction Based on Queuing Simulation
Fault-counting data are collected in the testing process of software development. However, the data are not used for evaluating the efficiency of fault correction activities because the information on the fault detection and correction times of each fault are not recorded in the fault-counting data. Furthermore, it is difficult to collect new data on the detection time of each fault to realize efficiency evaluation for fault correction activities from the collected fault-counting data due to the cost of personnel and data collection. In this paper, we apply the thinning method, using intensity functions of the delayed S-shaped and inflection S-shaped software reliability growth models (SRGMs) to generate sample data of the fault detection time from the fault-counting data. Additionally, we perform simulations based on the infinite server queuing model, using the generated sample data of the fault detection time to visualize the efficiency of fault correction activities.
Journal Article
A Software Reliability Model with Dependent Failure and Optimal Release Time
In the past, because computer programs were restricted to perform only simple functions, the dependence on software was not large, resulting in relatively small losses after a failure. However, with the development of the software market, the dependence on software has increased considerably, and software failures can cause significant social and economic losses. Software reliability studies were previously conducted under the assumption that software failures occur independently. However, as software systems become more complex and extremely large, software failures are becoming frequently interdependent. Therefore, in this study, a software reliability model is developed under the assumption that software failures occur in a dependent manner. We derive the software reliability model through the number of software failure and fault detection rate assuming point symmetry. The proposed model proves good performance compared with 21 previously developed software reliability models using three datasets and 11 criteria. In addition, to find the optimal release time, a cost model using the developed software reliability model was presented. To determine this release time, four parameters constituting the software reliability model were changed by 10%. By comparing the change in the cost model and the optimal release time, it was found that parameter b had the greatest influence.
Journal Article
A software reliability model for open source big data system considering fault introduction and fault removal efficiency
Reliability is critical to the stable operation of open source big data system software. So far, the reliability modeling and evaluation of open source big data system software is still in its early stages, and traditional software reliability models are not suitable for the development and testing environment of open source big data system software. Both closed source and open source software development and testing environments cannot be consistent with big data system software development and testing environments, and open source big data system software development and testing environments are even more complex. Such as, dynamic and heterogeneous environments, evolving architectures, and interdependencies and ecosystem complexity, etc. Therefore, traditional software reliability models fail to meet the reliability evaluation requirements for open source big data system software. In this paper, we focus on the characteristics of open source big data system software development and testing, such as the complexity of fault detection, the possibility of introducing new faults and fault removal efficiency factors during fault debugging, to establish a new software reliability model. By comparing with established software reliability models, the accuracy of the proposed model in predicting faults is verified, and the proposed model can effectively evaluate the reliability of open source big data system software. The proposed model can be used for fault prediction and reliability evaluation of open source big data system software in practical development.
Journal Article
Stochastic debugging based reliability growth models for Open Source Software project
Open Source Software (OSS) is one of the most trusted technologies for implementing industry 4.0 solutions. The study aims to assist a community of OSS developers in quantifying the product’s reliability. This research proposes reliability growth models for OSS by incorporating dynamicity in the debugging process. For this, stochastic differential equation-based analytical models are developed to represent the instantaneous rate of error generation. The fault introduction rate is modeled using exponential and Erlang distribution functions. The empirical applications of the proposed methodology are verified using the real-life failure data of the Open Source Software projects, GNU Network Object Model Environment, and Eclipse. A soft computing technique, Genetic Algorithm, is applied to estimate model parameters. Cross-validation is also performed to examine the forecasting efficacy of the model. The predictive power of the developed models is compared with various benchmark studies. The data analysis is conducted using the R statistical computing software. The results demonstrate the proposed models’ efficacy in parameter estimation and predictive performance. In addition, the optimal release time policy based on the proposed mathematical models is presented by formulating the optimization model that intends to minimize the total cost of software development under reliability constraints. The numerical illustration and sensitivity analysis exhibit the proposed problem's practical significance. The findings of the numerical analysis exemplify the proposed study's capability of decision-making under uncertainty.
Journal Article
A software reliability model for open source big data systems based on Weibull–Weibull distribution
With the development of society, big data technology has been integrated into people’s production and life. The reliability of software (or components) for big data systems has received attention from people. So far, there has been relatively little research on reliability modeling and assessment of the software for big data systems. Considering the notable rise in the quantity of errors found in the later phases of testing of open source big data system software during the software development and test, this paper proposes a software reliability model based on a Weibull–Weibull distribution. The experimental results verified the effectiveness of the proposed model and the accuracy of predicting remaining faults in the software. The model proposed in this study can help developers to assess software reliability during the development of open source big data systems.
Journal Article
Multi release software reliability modelling incorporating fault generation in detection process and fault dependency with change point in correction process
Many software reliability growth models (SRGMs) have been proposed in the literature to evaluate the remaining faults and software reliability. The probability of getting failure-free software within a specified period and environment is known as software reliability and is recognized as one of the essential aspects. In this paper, we present a new SRGM of the fault detection process (FDP) and fault correction process (FCP) and study the dependency between the FDP and FCP as the amount of fault dependency, not time dependency. The FDP is modeled by considering a multi-release concept where the leftover faults from the previous release and newly added faults after enhancing the existing features in the software are considered. Further, the FCP model is proposed by introducing the change point concept in the fault dependency function. These models are validated on two actual medium-sized software system data sets. The results show that the proposed models fit the data set more accurately than the existing SRGMs. We have also discussed the optimal release time through a cost model where setup, testing, and debugging costs are introduced in both the testing and operational phases in the cost model.
Journal Article