Based on the software reliability, combined with the usage profile and functional profile of software, the reliability computing method of the software system is presented in this paper. The reliability is used for guide the designing of the system structure and showing how this designing to response the user demand changing. Lastly, an example is used to show the application of this method.

Requirements analysis is the most important phase of the software life cycle process. Some studies have shown the most faults of software are from the requirements phase. Therefore, the quality of Software Requirements Specification has become the key to project success, which correctness, consistency, no ambiguity of software requirements specification is more important. This paper presents a classification method based on natural language processing techniques and grey similar correlation. The first step of this method is that keywords refined from various functional requirements through segmentation of natural language processing, thus made up of heavy weight vector based on the weight of functional requirements, such a functional requirement corresponds to a weight vector. The second step is that the related technology of grey system is used to compute grey correlation coefficient between two weight vectors, in order to construct a correlation matrix. Finally, the appropriate statistical tools are used to classify functional requirements statements. The clustering results based on this method can provide work guidance for requirement analysts, software developers, software testers, software maintenance. 

In this paper, We used characteristic of the Unified Modeling Language （UML） use case model in describing the function of the system＇ s and the iteration sequence of agile development process to obtain a new iteration sequence based on the risk an value of the various use cases in UML use case diagrams. An obvious advantage of doing so is to ensure high-risk and high-value use cases will be first developed, so that the use cases will be repeatedly tested in the next iteration, thereby increasing overall system reliability.

The purpose of this paper is to use the most widely used formal language-VDM ＋ ＋ that has characteristics of accuracy and consistent in describing syntax and semantic of the system model, combined with VDMTOOLS and Rational Rose tool to transform the various elements of the UML class model into representation of VI）M＋ ＋. This method achieves the various elements contained UML class model about the syntax and semantics checking, and further improves the quality of UML modeling.

Many faults come from the design phase. In order to improve the maintainability of the software, the design of software architecture must be adopted modular design. This paper presents a method of UML class diagram translated into a directed complex network with weight value. The relation weight coefficient matrix between classes can be calculated by Dijkstra algorithm. The clustering algorithm is implemented on the relation weight coefficient matrix. The result of clustering analysis is that the closely related classes can be clustered into a component. Finally, modular design of the software system can be realized. © 2017 IEEE.

Software reliability growth modeling plays an important role in software reliability evaluation. To incorporate more information and provide more accurate analysis, modeling software fault detection and correction processes has attracted widespread research attention recently. However, the assumption of the stochastic fault correction time delay brings more difficulties in modeling and estimating the parameters. In practice, other than the grouped fault data, software test records often include some more detailed information, such as the rough time when one fault is detected or corrected. Such semi-grouped dataset contains more information about fault removal processes than commonly used grouped dataset. Using the semi-grouped datasets can improve the accuracy of time delayed models. In this paper, a fault removal modelling framework for software reliability with semi-grouped data is studied and extended into multi-released software. Also, the corresponding parameter estimation is carried out with Maximum Likelihood estimation method. One test dataset with three releases from a practical software project is applied with the proposed framework, which shows satisfactory performance with the results.

In this paper, the reliability model and the evaluation methods were neatly used with combining the engineering practice of the noise laboratory supply system, and this study give a quantitative evaluation result of the reliability system. It provides the scientific basis for improving the engineering design and increasing the reliability. This research results can be extended to similar projects, such as fuel supply system. From an engineering point of view, it is an innovation that the filter in the system is identified to be a "series - parallel mixed" structure by considering its physical structure and performance requirement.

Agile methods such as XP emphasize the adaptation to changes, the collaboration with customers and the role of individuals. The project manager, developer and customer should have a tactic to handle the uncertain factors and the management conflicts in software development process. XP ′ s on-site customer practice makes the customer play a important role in development process, and facilitate the broad collaboration between developer and customer. Based on the feature of XP project life cycle and practices, this paper explores respective activities and responsibilities between development team and customer team, and the issues in their collaboration. Some practices in which customer participates is addressed, the activities of both developer and customer are described, some probable problems when customer and programmer work together are presented, and a formal example is given. © 2008 IEEE.

 This paper proposes a Web Software Reliability Growth Model(SRGM) with BurrX Testing Effort Function(TEF) based on Non-homogeneous Poisson Process(NHPP) and develops its application to Web software reliability analysis. The incorporated TEF depicts the relationship between Web workload and calendar time, and solves the problem about high uneven of Web workload. By using the workload data and failure data from Web logs. Experimental results show that the proposed model can be used to describe the actual failure process more faithfully than G-O model, which is often used to analyze Web software.

Large software systems require regular upgrading that tries to correct the reported faults in previous versions and add some functions to meet new requirements. It is thus necessary to investigate changes in reliability in the face of ongoing releases. However, the current modeling frameworks mostly rely on the idealized assumption that all faults will be removed instantaneously and perfectly. In this paper, the failure processes in testing multi-release software are investigated by taking into consideration the delays in fault repair time based on a proposed time delay model. The model is validated on real test datasets from the software that has been released three times with new features. A comprehensive analysis of optimal release times based on cost-efficiency is also provided, which could help project managers to determine the best time to release the software. 

The masked data are the system failure data when the exact cause of the failures might be unknown. That is, it is the subset of components known to contain the component causing system failures. In general, the maximum likelihood estimation (MLE) of parameters are difficult to find when there exist masked data, because superposition non-homogenous Poisson process (NHPP) software reliability model cannot be decomposed into the independent NHPP models. In this paper, the MLE of software reliability from masked data is studied based on superposition NHPP models. Finally, numerical example based on simulation data is given to illustrate a good performance of MLE.

Masked data are the system failure data when the exact cause of the failures might be unknown. That is, the cause of the system failures may be any one of the components. Additionally, to incorporate more information and provide more accurate analysis, modeling software fault detection and correction processes have attracted widespread research attention recently. However, stochastic fault correction time and masked data brings more difficulties in parameter estimation. In this paper, a framework of open source software growth reliability model with masked data considering both fault detection and correction processes is proposed. Furthermore, a novel Expectation Least Squares (ELS) method, an EM-like (Expectation Maximization) algorithm, is used to solve the problem of parameter estimation, because of its mathematical convenience and computational efficiency. It is note that the ELS procedure is easy to use and useful for practical applications, and it just needs more relaxed hidden assumptions. Finally, three data sets from real open source software project are applied to the proposed framework, and the results show that the proposed reliability model is useful and powerful.

As the web-server based business is rapidly developed and popularized, how to evaluate and improve the reliability of web-servers has been extremely important. Although a large number of software reliability growth models (SRGMs), including those combined with multiple change-points (CPs), have been available, these conventional SRGMs cannot be directly applied to web software reliability analysis because of the complex web operational profile. To characterize the web operational profile precisely, it should be realized that the workload of a web server is normally non-homogeneous and often observed with the pattern of random impulsive shocks. A web software reliability model with random impulsive shocks and its statistical analysis method are developed. In the proposed model, the web server workload is characterized by a geometric Brownian motion process. Based on a real data set from IIS server logs of ICRMS website (www.icrms.cn), the proposed model is demonstrated to be powerful for estimating impulsive shocks and web software reliability.

The traditional reliability models cannot well reflect the effect of performance dependence of subsystems on the reliability of system, and neglect the problems of initial reliability and standby redundancy. In this paper, the reliability of a parallel system with active multicomponents and a single cold-standby unit has been investigated. The simultaneously working components are dependent and the dependence is expressed by a copula function. Based on the theories of conditional probability, the explicit expressions for the reliability and the MTTF of the system, in terms of the copula function and marginal lifetime distributions, are obtained. Let the copula function be the FGM copula and the marginal lifetime distribution be exponential distribution, a system with two parallel dependent units and a single cold-standby unit is taken as an example. The effect of different degrees of dependence among components on system reliability is analyzed, and the system reliability can be expressed as the linear combination of exponential reliability functions with different failure rates. For investigating how the degree of dependence affects the mean lifetime, furthermore, the parallel system with a single cold standby, comprising different number of active components, is also presented. The effectiveness of the modeling method is verified, and the method presented provides a theoretical basis for reliability design of engineering systems and physics of failure.

Storage reliability that measures the ability of products in a dormant state to keep their required functions is studied in this paper. Unlike the operational reliability, storage reliability for certain types of products may not be always 100% at the beginning of storage since there are existing possible initial failures that are normally neglected in the models of storage reliability. In this paper, a new combinatorial approach, the non-parametric measure for the estimates of the number of failed products and the current reliability at each testing time in storage, and the parametric measure for the estimates of the initial reliability and the failure rate based on the exponential reliability function, is proposed for estimating and predicting the storage reliability with possible initial failures. The proposed method has taken into consideration that, the initial failure and the reliability testing data before and during the storage process, are available for providing more accurate estimates of both initial failure probability and the probability of storage failures. When storage reliability prediction that is the main concern in this field should be made, the non-parametric estimates of failure numbers can be used into the parametric models for the failure process in storage. In the case of exponential models, the assessment and prediction method for storage reliability is provided in this paper. Finally, numerical examples are given to illustrate the method. Furthermore, a detailed comparison between the proposed method and the traditional method, for examining the rationality of assessment and prediction on the storage reliability, is presented. The results should be useful for planning a storage environment, decision-making concerning the maximum length of storage, and identifying the production quality.

Storage reliability is of importance for the products that largely stay in storage in their total life-cycle such as warning systems for harmful radiation detection, etc. Usually, the field-testing data can be available, but the failure causes for a series system cannot be always known because of the masked information. In this paper, the storage reliability model with possibly initial failures is studied on the statistical analysis method when the masked data are considered. To optimize the use of the masked survival data from storage systems, a technique based on the least squares (LS) method with an EM-like algorithm, is proposed for the series system. The parametric estimation procedure based on the LS method is developed by applying the algorithm to update the testing data, and then the LS estimation for the initial reliability and failure rate of the components constituting the series system are investigated. The results should be useful for accurately evaluating the production reliability, identifying the production quality, and planning a storage environment.

Storage reliability that measures the ability of products in a dormant state to keep their required functions is studied in this paper. For certain types of products, Storage reliability may not always be 100% at the beginning of storage, unlike the operational reliability, which exist possible initial failures that are normally neglected in the models of storage reliability. In this paper, a new integrated technique, the non-parametric measure based on the E-Bayesian estimates of current failure probabilities is combined with the parametric measure based on the exponential reliability function, is proposed to estimate and predict the storage reliability of products with possible initial failures, where the non-parametric method is used to estimate the number of failed products and the reliability at each testing time, and the parameter method is used to estimate the initial reliability and the failure rate of storage product. The proposed method has taken into consideration that, the reliability test data of storage products containing the unexamined before and during the storage process, is available for providing more accurate estimates of both the initial failure probability and the storage failure probability. When storage reliability prediction that is the main concern in this field should be made, the non-parametric estimates of failure numbers can be used into the parametric models for the failure process in storage. In the case of exponential models, the assessment and prediction method for storage reliability is presented in this paper. Finally, a numerical example is given to illustrate the method. Furthermore, a detailed comparison between the proposed and traditional method, for examining the rationality of assessment and prediction on the storage reliability, is investigated. The results should be useful for planning a storage environment, decision-making concerning the maximum length of storage, and identifying the production quality.

Reconfigurable manufacturing systems (RMSs) are considered the solution of choice when variable production capacity and functionality are required. A combinational approach, which integrates the steady-state probabilities of repairable reconfigurable machine tools (RMTs) and inventory-state probabilities of buffers through an improved universal generating function, is introduced in this study to assess the compound performance indicators (CPIs) of a repairable RMS. This paper contributes to the existing literature by considering the availability of buffers to calculate the CPIs of an RMS. In the proposed approach, the dynamic-state probability for each RMT is determined with a homogeneous continuous-time Markov model, and steady-state probability is obtained as the limit of the dynamic probability as time tends to infinity. In addition, a descriptive input-output information flow, which combines the conveying processes of the machined parts through buffers with the Poisson process, is proposed to determine the inventory-state probabilities of the buffers. Moreover, the explicit expressions of the CPI and expected performance rate (for the RMS and its constituent RMTs) are determined, and the validation procedure and technical details of the performance analysis for the Monte Carlo simulation are presented. Finally, a non-serial, repairable, multi-state RMS with multiple buffers that produces three types of engine cylinder heads is presented to validate the proposed approach. The simulation results verify the accuracy of the performance assessment of the RMS. It is useful for performance improvement in terms of machine reliability, resource utilisation efficiency, and decision-making concerning the configuration of RMS with buffers.

As the Wireless Sensor Networks(WSNs) are widely applied to various fields recent years, the quality of WSN has been increasingly concerned. Since reliability is the most important factor of quality, how to evaluate reliability of WSN through failure counting is our main subject. In this paper, we try to utilize NHPP theorem in the failure process of WSN and propose a reliability model based on NHPP. And also, the model validation is conducted by simulation. 

As the Wireless Sensor Networks(WSNs) are widely implemented in various fields recent years, the quality of WSNs has been increasingly concerned. WSNs can usually be divided into sub-nets, which assumed to work or fail independently. Through the failure data of those sub-nets, the additive NHPP model for reliability evaluation is composed, and then the maximum likelihood estimation is applied to estimate the unknown parameters in the model. Finally, the simulation shows that the additive NHPP model is better than general NHPP model under certain circumstances. 

This paper studies the reliability modelling of wireless sensor networks (WSNs) with the masked data that are often observed in practice. The masked data are the system failure data when exact subsystems or components causing system failures cannot be identified. When the masked data are observed, however, it is difficult to estimate the WSN reliability since the failure processes of the subnets cannot be decomposed into simple subsystem processes. In this paper, an additive non-homogeneous poisson process (NHPP) model is proposed to describe the failure process of the WSN with subnets. The maximum likelihood estimation (MLE) procedure is developed to estimate the parameters in the proposed model. By applying the given procedure, the WSN reliability estimate can be relatively easy to obtain. A numerical example based on simulation data with random masking is also provided to illustrate the applicability of the methodology. 

One of the widely used NHPP models in reliability is the so-called power-law model, also known as the Duane model. A power-law model can be applied in analyzing failure data of both software and hardware systems. Nevertheless, the power-law model is no longer applicable to describe the failure behavior when a hardware/software embedded system is concerned since the failures can come from both hardware and software. How to analyze the failure data of this type is still a problem to study and a new type of model is needed to develop. In this paper, we consider the superposition of the power-law models (SPLM) as one candidate to describe the failure behaviors of hardware/software systems. The characteristics of SPLMs are thoroughly studied. It is shown that an SPLM has the intensity function that can be increasing, decreasing, increasing-then-decreasing or decreasing-then-increasing. Specifically, the identification method of the superposition of power-law processes is provided by using the TTT-plot technique. The TTT-transform of a superposition of power-law processes is DFR-like. Therefore, the conditional TTT-plot should present a convex pattern if the system failures follow a superposition process by a few power-law processes. This provides us an easy way to identify the model when the testing data is available.

Wireless sensor networks (WSNs) have widely been applied in various industries and business fields covering large geographical regions. It is therefore important to be able to model, assess and predict the reliability of WSNs since the failures can have a great effect on the monitoring or control systems that are normally depending on the WSNs. In this paper, the general WSN reliability models are developed by deleting the independent assumption of component or subsystem failures and are consequently more reasonable to characterise the failure process of WSNs. The methodology in the proposed WSN reliability models is to consider that the failure times of subsystems are dependent variables and their joint distribution is obtained by binding their marginal failure distributions together through a copula function. For specific Frank copula functions, the Star-based WSN reliability models are derived and their properties are also discussed in this paper. 

In recent years, the prognostics and health management (PHM) technique has greatly been developed especially for electronic devices and systems. Little work, however, has been done on PHM techniques for software systems. During their total lifecycle, the software systems, due to various reasons such as new requirements or changes in technology, have to experience changes that can lead to a degeneration of the system's architecture. Therefore, it is important and necessary to identify and predict the software health status in order to avoid serious degeneration or erosion of system's architecture. In this paper, a software PHM approach based on Discriminant Coordinates Analysis (DCA) is proposed, and the health status of software systems is suggested to be categorized into five groups: Healthy, Normal, Sub-healthy, Unhealthy and Deteriorated. The proposed model based on software design metrics can help managers and developers predict and identify the software health status. Numerical example based on simulation data is given to illustrate its application on the modeling process.

Storage reliability is of importance for the products that largely stay in storage in their total life-cycle such as warning systems for harmful radiation detection, rescue systems, many kinds of defense systems, etc. The storage reliability of a product is commonly defined as the probability that the product can perform its specific function for a period of specific storage time under specific storage environment. Logically, the failures of the product in storage should be identified with the same criteria as in its operation process. However, the failure data in storage may be observed indirectly through the maintenance or inspection activities. Nevertheless, when the storage reliability is concerned in general, the reliability model should take into consideration the possibility that the operational reliability does not start at 100%, for example, the one-shot product may have only 96% operational reliability when they are newly produced. In this paper, the storage reliability model with possibly initial failures, which are usually neglected at the beginning of storage in most of storage models, is studied on the statistical analysis method when the masked data are observed. The parametric estimation procedure, based on the Least Squares method, is developed generally by applying an EM-like (Expectation and Maximization) algorithm for the storage data in which some information about which components have caused the system failures is not known, namely the failure data are masked. The estimates of the model parameters including the initial reliability are formalized. In the case of exponentially distributed storage lifetime and series system, a numerical example is provided to illustrate the method and procedure though the method is not limited to such case. The results should be useful for planning a storage environment, decision-making concerning the maximum length of storage, maintenance strategy optimization and identifying the production quality.

Environmental Stress Screening (ESS) is usually referred to the process of exposing a product to environmental stresses for detecting and eliminating latent defects made in manufacturing process. The common ESS profiles are using thermal cycling, random vibration or their combinations as the screening stresses. Various guidelines and standards have been available for determining an ESS profile. Nevertheless, there are cases that the standard ESS profiles may not be efficient enough to detect the latent defects in some manufacturing process. This paper reports the results of one industrial project on the ESS profile design for circuits. In the project, a comparative study was carried out for the evaluation of the ESS strength under the ESS profiles with incremental step stresses (ISS). The ISS profile under the study is firstly to implement a step thermal cycling and then perform the test with two screening stimuli concurrently: ordinary thermal cycling and random vibration of step increasing intensity levels. For the purpose of the comparative study, the ESS test samples were consisted of two groups A and B that were made at two different levels of manufacturing: soldering by skilled and unskilled workers, respectively. The comparative study shows that the ISS profiles are effective for exposing the latent defects in sample group A and B. Based on the project results, an ESS profile is recommended in this paper.