Reliability Growth Research Articles

Detecting Fake Reviews Using Multidimensional Representations With Fine-Grained Aspects Plan

Due to the rapid growth of network data, the authenticity and reliability of network information have become increasingly important and have presented challenges. Most of the methods for fake review detection start with textual features and behavioral features. However, they are time-consuming and easily detected by fraudulent users. Although most of the existing neural network-based methods address the problems presented by the complex semantics of reviews, they do not account for the implicit patterns among users, reviews, and products; additionally, they do not consider the usefulness of information regarding fine-grained aspects in identifying fake reviews. In this paper, we propose an attention-based multilevel interactive neural network model with aspect constraints that mines the multilevel implicit expression mode of reviews and integrates four dimensions, namely, users, review texts, products and fine-grained aspects, into review representations. We model the relationships between users and products and use these relationships as a regularization term to redefine the model’s objective function. The experimental results from three public datasets show that the model that we propose is superior to the state-of-the-art methods; thus showing the effectiveness and portability of our model.

Practical reliability growth modeling

AbstractReliability growth models are commonly used in the Department of Defense (DoD) to plan, track, and project reliability during system acquisition and testing. We describe two commonly used classes of reliability growth models for continuous failure time data and the metrics appropriate for their use. We also present two Bayesian reliability growth models that are based on the DoD models. The Bayesian models are easily interpretable in a statistical framework, which supports estimation and uncertainty quantification. Our goal is to provide a practical understanding of the development, implementation, and use of reliability growth models across a sequence of DoD testing events.

A software reliability growth model with Gompertz-logarithmic failure time distribution

PurposeThe Gompertz curve has been used in industry to estimate the number of remaining software faults. This paper aims to introduce a family of distributions for fitting software failure times which subsumes the Gompertz distribution.Design/methodology/approachThe mean value function of the corresponding non-homogenous Poisson process software reliability growth model is presented. Model parameters are estimated by the method of maximum likelihood. A comparison of the new model with eight models that use well-known failure time distributions of exponential, gamma, Rayleigh, Weibull, Gompertz, half normal, log-logistic and lognormal is performed according to the several statistical and informational criteria. Moreover, a Shannon entropy approach is used for ranking and model selection.FindingsNumerical experiments are implemented on five real software failure datasets varying from small to large datasets. The results exhibit that the proposed model is promising and particularly outperforms the Gompertz model in all considered datasets.Originality/valueThe proposed model provides optimized reliability estimation.

Software Shipping Time Estimation for Markovian Change-Point Reliability Model

A Markovian software reliability model is known as one of the useful models which enable us to describe the details on the dynamic behavior of software reliability growth process with imperfect debugging activities. As the application of this model for software development management, we discuss software shipping time estimation based on the extended Markovian software reliability model considering with the effect of change-point, which is the testing-time when the characteristic of the software failure-occurrence or fault-detection phenomenon changes notably. Especially in our discussion on the software shipping time estimation, we derive an analytical software shipping policy under certain software reliability objective and show numerical examples of the application of the software shipping policy by using actual software reliability data. Finally, we give some useful consideration on the relationship between the software shipping time and the software reliability growth process for conducing quality-oriented software development management.

RETRACTED: Software Reliability Growth Fault Correction Model Based on Machine Learning and Neural Network Algorithm

Dependence is one of programming quality points can be quantified. Software Reliability Growth Model (SRGM) used to study the reliability in various difficult times available. In all cases the test execution over time, the traditional time-sensitive SRGM may not be accurate. To overcome this problem, SRGM practice rather than using the test of time. Previously, the proposed limited testing athletic ability, but with unlimited test time, test motion becomes infinite and may not make sense. Then, this method presents endless test execution work related to older models of Neural Heterogeneous Poisson Process (NHPP) of. Programming model disappointment with the proposed information to prepare Artificial Neural Network (ANN). They can imagine a lot of the load placed on a similar model to represent the previous disappointment information. Use AI model selection method can adequately represent the past and future load fitting location information. Consider using common-sense information programming disappointed indicators to show the proposed number of Testing Exertion Flow (TEF) and SRGM also successfully depicted disappointment extensive information, Machine Learning (ML) and ANN improved the accuracy of the conventional boundary estimation as compared with the discharge time and can be used to ensure that programming.

Correction to: Optimal release time determination via fuzzy goal programming approach for SDE-based software reliability growth model

While typesetting the article, the affiliation for the corresponding author Bhagyashree Chaudhuri has been affiliation twice. The correct affiliation has been copied below.

Analysis of correlated multivariate degradation data in accelerated reliability growth

AbstractModern engineering systems have become increasingly complex and at the same time are expected to be developed faster. To shorten the product development time, organizations commonly conduct accelerated testing on a small number of units to help identify failure modes and assess reliability. Many times design changes are made to mitigate or reduce the likelihood of such failure modes. Since failure‐time data are often scarce in reliability growth programs, existing statistical approaches used for predicting the reliability of a system about to enter the field are faced with significant challenges. In this work, a statistical model is proposed to utilize degradation data for system reliability prediction in an accelerated reliability growth program. The model allows the components in the system to have multiple failure modes, each associated with a monotone stochastic degradation process. To take into account unit‐to‐unit variation, the random effects of degradation parameters are explicitly modeled. Moreover, a mean‐degradation‐stress relationship is introduced to quantify the effects of different accelerating variables on the degradation processes, and a copula function is utilized to model the dependency among different degradation processes. Both a maximum likelihood (ML) procedure and a Bayesian alternative are developed for parameter estimation in a two‐stage process. A numerical study illustrates the use of the proposed model and identifies the cases where the Bayesian method is preferred and where it is better to use the ML alternative.

Optimal release time determination via fuzzy goal programming approach for SDE-based software reliability growth model

Faults in software can be of varied severity and so is the priority with which they are fixed. Hence, these two factors impact the reliability of the software to a large extent and is worthy of being taken into consideration while framing the software reliability growth model (SRGM). In this paper, a stochastic differential equation-based SRGM has been formulated where detection of faults has been associated with severity of fault occurrence and the process of correcting them have been knit with priority of defect fixing. Another matter of concern is the determination of the optimal time to release the software in the market with high reliability figures keeping the development cost margin minimum. Release time determination in the literature is done mainly using crisp or type-1 fuzzy system approach wherein the cost constraint in the optimization framework is considered to be of deterministic nature which is something highly unrealistic. In this study, the cost constraint is considered to comprise of a randomized cost budget with variable coefficients being interval type-2 fuzzy numbers which can represent the dynamicity associated with changes in specification during software development process in a much more realistic fashion. This encourages the release time determination problem to be framed as a multi-objective chance constrained optimization problem, which is thereby solved using fuzzy goal programming approach based on Taylor series method. The proposed SRGM and the release time approach have been validated on real time data, which show much promising results than many of those available in the literature.

A new perspective of link prediction in complex network for improving reliability

Link prediction is an important issue for network evolution. For many real networks, future link prediction is the key to network development. Experience shows that improving reliability is an important trend of network evolution. Therefore, we consider it from a new perspective and propose a method for predicting new links of evolution networks. The proposed network reliability growth (NRG) model comprehensively considers the factors related to network structure, including the degree, neighbor nodes and distance. Our aim is to improve the reliability in link prediction. In experiments, we apply China high-speed railway network, China highway network and scale-free networks as examples. The results show that the proposed method has better prediction performance for different evaluation indexes. Compared with the other methods, such as CN, RA, PA, ACT, CT and NN, the proposed method has large growth rate and makes the reliability reach the maximum at first which save network construction resources, cost and improve efficiency. The proposed method tends to develop the network towards homogeneous network. In real networks, this structure with stronger stability is the goal of network construction. Therefore, our method is the best to improve network reliability quickly and effectively.

A dynamically weighted discrete combination model for all releases of a software system

About 50% of the total cost of developing software is the cost of its testing and debugging. Software Reliability Growth models—which play an important role in planning and managing testing activity—are found to be appropriate in determining when the error levels are low enough to stop debugging. Although many Software Reliability Growth Models (SRGMS) have been proposed as continuous time models to plan and execute testing, there is a need to evolve discrete models to cater to discrete data. SRGMS have evolved over the years from simple to flexible models. This paper proposes the combination of two simple fault-based models to create a dynamically weighted model which describes both concave and S-shaped growth of mean value functions, occurring either in the whole project or in parts thereof. Most importantly, this combination model can be successfully deployed without modification for testing all the releases of a software product. On validation with a number of discrete datasets and releases, this model is found to provide satisfactory goodness of fit. In addition to being adequate for past failure data, predictive validity measures have confirmed that the proposed model will be equally adequate for future data also.

Testing coverage based reliability modeling for multi-release open-source software incorporating fault reduction factor

Testing represents an important and necessary phase in the development process of a software that provides a reliable and efficient software product. To enhance software reliability, fault detection and correction are carried out during this process. In order to develop highly reliable software system software reliability growth models (SRGMs) are observed to be the most effective way. These SRGMs have been developed under a set of assumptions, there are many real-life factors that control the reliability of the software system during the testing process. In that context Fault Reduction Factor (FRF) and testing coverage are the most important key factors. In our proposed study we have developed SRGMs based on these environmental factors. We have also discussed the concept of multi releases of an Open-Source Software (OSS) project as a single release of the product may not meet the ever-increasing requirements of a competitive market. Therefore, this paper talks about the impact of different parameters such as FRF and testing coverage on the reliability of the different releases of an OSS. The mathematical expression based on these assumptions is provided by two models. These models assume the FRF as constant and testing coverage is assumed to have Weibull and Exponentiated Weibull distribution functions. The proposed models are then validated on real-life faults datasets and their performance is measured on the basis of different criteria of the goodness of fit.

Performance degradation analysis of reliability growth of airborne power supply based on logistic regression

With the improvement of reliability design of nonlinear airborne power supply system, it is more difficult to analyze the performance degradation of components under lean failure data in reliability growth test. In this paper, a state space model of performance degradation was established for nonlinear airborne power system, and the method of performance degradation modeling and analysis was studied by using nonlinear logistic regression process. Taking electrical, mechanical and thermal stress as characteristic failure loads, a nonlinear logistic regression analysis model was established to describe the performance degradation under comprehensive stress. The state of the degradation space is divided by the Markov distance, and the performance degradation trajectory is transformed into the degradation state space by using the maximum likelihood model and correlation degree model of observed variables, the degradation space trajectory of the performance of the characteristic parameters under multi-input variables is obtained. The correctness and validity of the nonlinear logistic regression model and the degradation process analysis method are verified by the test on the brushless motor reliability test platform.

An efficient parameter optimization of software reliability growth model by using chaotic grey wolf optimization algorithm

Software reliability growth model (SRGM) with modified testing-effort function (TEF) is a function to evaluate and foresee the parameters of the data. Reliability of software is portrayed as the distinct possibility that for a predefined time, a software package will continue to run on an advance domain without frustration. SRGM utilized a few optimization procedure algorithms to advance the parameters by bifurcating them into a few stages however to upgrade the technique by using all of the parameters at the same time, the algorithm utilized is the chaotic grey wolf optimization algorithm (CGWO). CGWO is an advanced heuristic system for portraying the execution by achieving complex parameter optimization and designing application issues. Different parametric reliabilities rely upon the attributes or characteristics of the data. The parameters are predicted using the Pham–Zhang (PZ) model. Tandem computer software dataset DS1 and DS2 are used to compare the predicted parameter of SRGM obtained by Pham–Zhang (PZ) model using testing effort functions (TEFs) based on the evaluation metrics mean square error (MSE), relative error (RE) and coefficient of determination (R2). To enhance the reliability of SRGM, the parameters of SRGM estimated using TEF and enhanced using chaotic maps to improve search performance. By using the constrained benchmark functions the results of chaotic maps are obtained. Based on the chaotic graph results, the Chebyshev graph shows a good convergence rate of 78%. Overall, 86% of the results revealed an association between the choice variable and fitness criteria for CGWO. In the SRGM using CGWO, the expected result is completely mechanized and does not require any client necessity.

Comprehensive analysis of hybrid nature-inspired algorithms for software reliability analysis

Software reliability growth model accuracy could be justified only with the help of their parameter estimation capability. Closer are the estimated parameters to the actual failure dataset higher will be the accuracy of that software reliability model. Inaccurate estimate of the parameters by the software reliability growth model may lead to heavy losses. Authors in this paper recognized nature inspired meta-heuristic algorithm based effective methods for parameter optimization of software reliability models. Both interval domain and time domain datasets have been used for software reliability model parameter estimation process and experiments have been conducted using real project datasets. Results are analyzed using actual number of failures given in real datasets and compared among various existing meta-heuristic algorithms. In this paper, authors also identified the use of hybrid meta-heuristic techniques in comparison to other meta-heuristic algorithms for software reliability assessment and evaluated them based on various performance criteria. Based on the results it is obtained that hybrid algorithms are very much satisfactory in terms of accuracy in parameter estimation as compared to their counterpart and might be used on other software reliability models in-order to assess reliability of a system with higher accuracy.

Software Reliability Model with Dependent Failures and SPRT

Software reliability and quality are crucial in several fields. Related studies have focused on software reliability growth models (SRGMs). Herein, we propose a new SRGM that assumes interdependent software failures. We conduct experiments on real-world datasets to compare the goodness-of-fit of the proposed model with the results of previous nonhomogeneous Poisson process SRGMs using several evaluation criteria. In addition, we determine software reliability using Wald’s sequential probability ratio test (SPRT), which is more efficient than the classical hypothesis test (the latter requires substantially more data and time because the test is performed only after data collection is completed). The experimental results demonstrate the superiority of the proposed model and the effectiveness of the SPRT.

Fault data screening and failure rate prediction framework-based bathtub curve on industrial robots

PurposeThis study aims to obtained the failure probability distributions of subsystems for industrial robot and filtrate its fault data considering the complicated influencing factors of failure rate for industrial robot and numerous epistemic uncertainties.Design Methodology ApproachA fault data screening method and failure rate prediction framework are proposed to investigate industrial robot. First, the failure rate model of the industrial robot with different subsystems is established and then the surrogate model is used to fit bathtub curve of the original industrial robot to obtain the early fault time point. Furthermore, the distribution parameters of the original industrial robot are solved by maximum-likelihood function. Second, the influencing factors of the new industrial robot are quantified, and the epistemic uncertainties are refined using interval analytic hierarchy process method to obtain the correction coefficient of the failure rate.FindingsThe failure rate and mean time between failure (MTBF) of predicted new industrial robot are obtained, and the MTBF of predicted new industrial robot is improved compared with that of the original industrial robot.Research Limitations ImplicationsFailure data of industrial robots is the basis of this prediction method, but it cannot be used for new or similar products, which is the limitation of this method. At the same time, based on the series characteristics of the industrial robot, it is not suitable for parallel or series-parallel systems.Practical ImplicationsThis investigation has important guiding significance to maintenance strategy and spare parts quantity of industrial robot. In addition, this study is of great help to engineers and of great significance to increase the service life and reliability of industrial robots.Social ImplicationsThis investigation can improve MTBF and extend the service life of industrial robots; furthermore, this method can be applied to predict other mechanical products.Originality ValueThis method can complete the process of fitting, screening and refitting the fault data of the industrial robot, which provides a theoretic basis for reliability growth of the predicted new industrial robot. This investigation has significance to maintenance strategy and spare parts quantity of the industrial robot. Moreover, this method can also be applied to the prediction of other mechanical products.

A Comparative Study on the Reliability Growth Enhancement Activities Using “ANALYSIS” and “TEST” through FMECA and Highly Accelerated Life Tests

A Comparative Study on the Reliability Growth Enhancement Activities Using “ANALYSIS” and “TEST” through FMECA and Highly Accelerated Life Tests

Stability Assessment Method Considering Fault Fixing Time in Open Source Project

Recently, open source software (OSS) are adopted various situations because of quick delivery, cost reduction and standardization of systems. Many OSS are developed under the peculiar development style known as bazaar method. According to this method, faults are detected and fixed by users and developers around the world, and the fixed result will be reflected in the next release. Also, the fix time of faults tends to be shorter as the development of OSS progresses. However, several large-scale open source projects have a problem that faults fixing takes a lot of time because faults corrector cannot handle many faults reports quickly. Furthermore, imperfect fault fixing sometimes occurs because the fault fixing is performed by various people and environments. Therefore, OSS users and project managers need to know the stability degree of open source projects by grasping the fault fixing time. In this paper, for assessment stability of large-scale open source project, we derive the imperfect fault fixing probability and the transition probability distribution. For derivation, we use the software reliability growth model based on the Wiener process considering that the fault fixing time in open source projects changes depending on various factors such as the fault reporting time and the assignees for fixing faults. In addition, we applied the proposed model to actual open source project data and examined the validity of the model.

A generalized multiple environmental factors software reliability model with stochastic fault detection process

Software systems have been widely applied in numerous safety–critical domains; however, large-scale software development is still considered as a complicated and expensive activity. As the latest trends in software industry accelerate the complexity and dependency of software development, such complicated and human-centered process needs to be addressed well. Meanwhile, recent survey investigations (Zhu et al. in J Syst Softw 109:150–160, 2015; Zhu and Pham in J Syst Softw 132:72–84, 2017) revealed that environmental factors, defined from software development, have significant impacts on software reliability. Considering such significant impacts, we first propose a generalized multiple-environmental-factors software reliability growth model with multiple environmental factors and the associated randomness under the martingale framework. The randomness is reflected on the process of detecting software fault. Indeed, this is a stochastic fault detection process. As an illustration, a specific multiple-environmental-factors software reliability growth model incorporating two specific environmental factors, percentage of reused modules and frequency of program specification change, is further developed. Lastly, we employ two real-world data sets to demonstrate the prediction performance of the proposed generalized multiple-environmental-factors software reliability growth model.

Use Piecewise Crow-AMSAA Method to Predict Infection and Death of Corona virus in Iran

Reliability growth is the positive improvement in a product’s criteria (or parameter) over a period of time due to changes in the design or product process. By analyzing the growth of reliability in a system, it can be seen that at a certain stage of the epidemic, the growth of the transmission and the rate of infection change over time. During the spread of disease, problem areas are identified and knowledge of the disease increased and then initial treatment and tools may be redesigned or reprocessed to take appropriate corrective action. In other words, each stage of the spread of the disease has a different level of growth transmission depending on appropriate corrective action. Therefore, according to this case, there are conditions under which phenomena can be described by Non-Homogeneous Poisson Process (NHPP). However, traditional epidemiological models based on exponential distribution are not able to predict disease growth during different stages of the outbreak. Therefore, in this paper, the Piecewise Crow-AMSAA (NHPP) model, which is based on the Non-Homogeneous Poisson process, is used to predict the growth of infected cases and deaths of Coronavirus outbreak. Initially, the Iran cumulative confirmed case and death data are divided into several sections based on the manual separation to find out each different infection phase at each different time period. Then Crow-AMSAA (NHPP) model is applied to the segmented data. At each stage of the outbreak, the model parameters are estimated independently using the maximum likelihood estimation (MLE) technique. Finally, the growth parameters in each stage are compared with each other and external and environmental factors are identified and examined.