Optimal Release Policy Research Articles

지수 및 비지수족 분포 모형에 근거한 소프트웨어 최적방출시기에 관한 비교 연구

본 연구에서는 소프트웨어 제품을 개발하여 테스팅을 거친 후 사용자에게 인도하는 시기를 결정하는 방출문제에 대하여 연구 하였다. 방출문제의 분포는 소프트웨어의 결함을 제거하거나 수정 작업 중에도 새로운 결함이 발생될 가능성이 있는 무한고장수를 가진 비동질적인 포아송 과정에 기초하였다. 수명분포는 지수 족 분포와 비지수족 분포를 이용한 최적 방출시기에 관한 문제를 비교 제시하였다. 이러한 지수 및 비지수분포에 근거하여 소프트웨어 요구 신뢰도를 만족시키고 소프트웨어 개발 및 유지 총비용을 최소화 시키는 최적 소프트웨어 방출 정책에 대하여 논의 되었다. 본 논문의 수치적인 예에서는 고장 간격 시간 자료를 적용하고 모수추정 방법은 최우추정법을 이용하고 추세분석을 통하여 자료의 효율성을 입증한 후 최적 방출시기를 추정하였다. Decision problem called an optimal release policies, after testing a software system in development phase and transfer it to the user, is studied. The applied model of release time exploited infinite non-homogeneous Poisson process. This infinite non-homogeneous Poisson process is a model which reflects the possibility of introducing new faults when correcting or modifying the software. The failure life-cycle distribution used exponential and non-exponential family which has various intensity. Thus, software release policies which minimize a total average software cost of development and maintenance under the constraint of satisfying a software reliability requirement becomes an optimal release policies. In a numerical example, after trend test applied and estimated the parameters using maximum likelihood estimation of inter-failure time data, estimated software optimal release time.

강도함수 특성에 근거한 소프트웨어 최적 방출시기에 관한 비교 연구

Abstract In this paper, we were researched decision problem called an optimal release policies after testing a software system in development phase and transferring it to the user. The applied model of release time exploited infinite failure non-homogeneous Poisson process This infinite failure non-homogeneous Poisson process is a model which reflects the possibility of introducing new faults when correcting or modifying the software. The intensity function used Gompertz, Preto and Log-logstic pattern which has the efficient various property. Thus, optimal software release policies which minimize a total average software cost of development and maintenance under the constraint of satisfying a software reliability requirement becomes an optimal release policies. In a numerical example, after trend test applied and estimated the parameters using maximum likelihood estimation of inter-failure time data, estimated software optimal release time. Key Words : Software Release Policies, Infinite Non-Homogeneous Poisson Process, Intensity Function Property

Incorporating Generalized Modified Weibull TEF in to Software Reliability Growth Model and Analysis of Optimal Release Policy

Software reliability is generally a key factor in software quality. Reliability is an essential ingredient in customer satisfaction. In software development process reliability conveys the information to managers to access the testing effort and time at which software release into the market. Large numbers of papers are published in this context. In this paper we proposed a software reliability growth model with generalized modified weibull testing effort. Performance application of proposed model is demonstrated through real datasets. The experimental results shown that the model gives an excellent performance compared to other models. We also discuss the optimal release time based on reliability requirement and cost criteria.

와이블 분포 특성에 근거한 소프트웨어 최적 방출시기에 관한 비교 연구

본 연구에서는 소프트웨어 제품을 개발하여 테스팅을 거친 후 사용자에게 인도하는 시기를 결정하는 방출문제에 대하여 연구하였다. 인도시기에 관한 모형은 무한 고장수에 의존하는 비동질적인 포아송 과정을 적용하였다. 이러한 포아송 과정은 소프트웨어의 결함을 제거하거나 수정 작업 중에도 새로운 결함이 발생될 가능성을 반영하는 모형이다. 고장발생 수명분포는 여러 분포들을 적합시키는데 효율적인 특성을 가진 와이블분포를 이용하였다. 따라서 소프트웨어 요구 신뢰도를 만족시키고 소프트웨어 개발 및 유지 총비용을 최소화시키는 방출시간이 최적 소프트웨어 방출 정책이 된다. 본 논문의 수치적인 예에서는 고장 간격 시간 자료를 적용하고 모수추정 방법은 최우추정법과 추세분석을 통하여 자료의 효율성을 입증한 후 최적 방출시기를 추정하였다. In this paper, we were researched decision problem called an optimal release policies after testing a software system in development phase and transferring it to the user. The applied model of release time exploited infinite failure non-homogeneous Poisson process This infinite failure non-homogeneous Poisson process is a model which reflects the possibility of introducing new faults when correcting or modifying the software. The failure life-cycle distribution used the Weibull distribution which has the efficient various property which has the place efficient quality. Thus, optimal software release policies which minimize a total average software cost of development and maintenance under the constraint of satisfying a software reliability requirement becomes an optimal release policies. In a numerical example, after trend test applied and estimated the parameters using maximum likelihood estimation of inter-failure time data, estimated software optimal release time.

A hydrographical network optimization under flood conditions

AbstractA decision support system (DSS) is developed for integrated, real-time flood control in a multi-reservoir system. The DSS integrates a geographic information system (GIS) with an optimization module, and a graphical dialog interface to allow an effective use by system operators. A Neuro-dynamic programming technique (NDP), which overcomes numerous limitations of stochastic dynamic programming (SDP), is introduced to determine the optimal release policy under flood conditions. Results obtained in a real-world case study are finally presented.

Optimization model of an irrigation reservoir for water allocation and crop planning under various weather conditions

This paper develops a non-linear programming optimization model with an integrated soil water balance, to determine the optimal reservoir release policies, the irrigation allocation to multiple crops and the optimal cropping pattern in irrigated agriculture. Decision variables are the cultivated area and the water allocated to each crop. The objective function of the model maximizes the total farm income, which is based on crop–water production functions, production cost and crop prices. The proposed model is solved using the simulated annealing (SA) global optimization stochastic search algorithm in combination with the stochastic gradient descent algorithm. The rainfall, evapotranspiration and inflow are considered to be stochastic and the model is run for expected values of the above parameters corresponding to different probability of exceedence. By combining various probability levels of rainfall, evapotranspiration and inflow, four weather conditions are distinguished. The model takes into account an irrigation time interval in each growth stage and gives the optimal distribution of area, the water to each crop and the total farm income. The outputs of this model were compared with the results obtained from the model in which the only decision variables are cultivated areas. The model was applied on data from a planned reservoir on the Havrias River in Northern Greece, is sufficiently general and has great potential to be applicable as a decision support tool for cropping patterns of an irrigated area and irrigation scheduling.

The exponentiated Weibull software reliability growth model with various testing‐efforts and optimal release policy

PurposeThe purpose of this research is to incorporate the exponentiated Weibull testing‐effort functions into software reliability modeling and to estimate the optimal software release time.Design/methodology/approachThis paper suggests a software reliability growth model based on the non‐homogeneous Poisson process (NHPP) which incorporates the exponentiated Weibull (EW) testing‐efforts.FindingsExperimental results on actual data from three software projects are compared with other existing models which reveal that the proposed software reliability growth model with EW testing‐effort is wider and effective SRGM.Research limitations/implicationsThis paper presents a SRGM using a constant error detection rate per unit testing‐effort.Practical implicationsSoftware reliability growth model is one of the fundamental techniques to assess software reliability quantitatively. The results obtained in this paper will be useful during the software testing process.Originality/valueThe present scheme has a flexible structure and may cover many of the earlier results on software reliability growth modeling. In general, this paper also provides a framework in which many software reliability growth models can be described.

Modeling and Analysis of Software Fault Detection and Correction Process by Considering Time Dependency

Software reliability modeling & estimation plays a critical role in software development, particularly during the software testing stage. Although there are many research papers on this subject, few of them address the realistic time delays between fault detection and fault correction processes. This paper investigates an approach to incorporate the time dependencies between the fault detection, and fault correction processes, focusing on the parameter estimations of the combined model. Maximum likelihood estimates of combined models are derived from an explicit likelihood formula under various time delay assumptions. Various characteristics of the combined model, like the predictive capability, are also analyzed, and compared with the traditional least squares estimation method. Furthermore, we study a direct, useful application of the proposed model & estimation method to the classical optimal release time problem faced by software decision makers. The results illustrate the effect of time delay on the optimal release policy, and the overall software development cost.

A study of software reliability growth from the perspective of learning effects

For the last three decades, reliability growth has been studied to predict software reliability in the testing/debugging phase. Most of the models developed were based on the non-homogeneous Poisson process (NHPP), and S-shaped type or exponential-shaped type of behavior is usually assumed. Unfortunately, such models may be suitable only for particular software failure data, thus narrowing the scope of applications. Therefore, from the perspective of learning effects that can influence the process of software reliability growth, we considered that efficiency in testing/debugging concerned not only the ability of the testing staff but also the learning effect that comes from inspecting the testing/debugging codes. The proposed approach can reasonably describe the S-shaped and exponential-shaped types of behaviors simultaneously, and the results in the experiment show good fit. A comparative analysis to evaluate the effectiveness for the proposed model and other software failure models was also performed. Finally, an optimal software release policy is suggested.

Reliability growth modeling and optimal release policy under fuzzy environment of an N-version programming system incorporating the effect of fault removal efficiency

Failure of a safety critical system can lead to big losses. Very high software reliability is required for automating the working of systems such as aircraft controller and nuclear reactor controller software systems. Fault-tolerant softwares are used to increase the overall reliability of software systems. Fault tolerance is achieved using the fault-tolerant schemes such as fault recovery (recovery block scheme), fault masking (N-version programming (NVP)) or a combination of both (Hybrid scheme). These softwares incorporate the ability of system survival even on a failure. Many researchers in the field of software engineering have done excellent work to study the reliability of fault-tolerant systems. Most of them consider the stable system reliability. Few attempts have been made in reliability modeling to study the reliability growth for an NVP system. Recently, a model was proposed to analyze the reliability growth of an NVP system incorporating the effect of fault removal efficiency. In this model, a proportion of the number of failures is assumed to be a measure of fault generation while an appropriate measure of fault generation should be the proportion of faults removed. In this paper, we first propose a testing efficiency model incorporating the effect of imperfect fault debugging and error generation. Using this model, a software reliability growth model (SRGM) is developed to model the reliability growth of an NVP system. The proposed model is useful for practical applications and can provide the measures of debugging effectiveness and additional workload or skilled professional required. It is very important for a developer to determine the optimal release time of the software to improve its performance in terms of competition and cost. In this paper, we also formulate the optimal software release time problem for a 3VP system under fuzzy environment and discuss a the fuzzy optimization technique for solving the problem with a numerical illustration.

Optimal Release of Inventory Using Online Auctions: The Two Item Case

In this paper we analyze policies for optimally disposing inventory using online auctions. We assume a seller has a fixed number of items to sell using a sequence of, possibly overlapping, single-item auctions. The decision the seller must make is when to start each auction. The decision involves a trade-off between a holding cost for each period an item remains unsold, and a higher expected final price the fewer the number of simultaneous auctions underway. Consequently the seller must trade-off the expected marginal gain for the ongoing auctions with the expected marginal cost of the unreleased items by further deferring their release. We formulate the problem as a discrete time Markov Decision Problem and consider two cases. In the first case we assume the auctions are guaranteed to be successful, while in the second case we assume there is a positive probability that an auction receives no bids. The reason for considering these two cases are that they require different analysis. We derive conditions to ensure that the optimal release policy is a control limit policy in the current price of the ongoing auctions, and provide several illustration of results. The paper focuses on the two item case which has sufficient complexity to raise challenging questions.

Software Reliability Analysis by Considering Fault Dependency and Debugging Time Lag

Over the past 30 years, many software reliability growth models (SRGM) have been proposed. Often, it is assumed that detected faults are immediately corrected when mathematical models are developed. This assumption may not be realistic in practice because the time to remove a detected fault depends on the complexity of the fault, the skill and experience of personnel, the size of debugging team, the technique(s) being used, and so on. During software testing, practical experiences show that mutually independent faults can be directly detected and removed, but mutually dependent faults can be removed iff the leading faults have been removed. That is, dependent faults may not be immediately removed, and the fault removal process lags behind the fault detection process. In this paper, we will first give a review of fault detection & correction processes in software reliability modeling. We will then illustrate the fact that detected faults cannot be immediately corrected with several examples. We also discuss the software fault dependency in detail, and study how to incorporate both fault dependency and debugging time lag into software reliability modeling. The proposed models are fairly general models that cover a variety of known SRGM under different conditions. Numerical examples are presented, and the results show that the proposed framework to incorporate both fault dependency and debugging time lag for SRGM has a better prediction capability. In addition, an optimal software release policy for the proposed models, based on cost-reliability criterion, is proposed. The main purpose is to minimize the cost of software development when a desired reliability objective is given

Bayesian Approach to Optimal Release Policy of Software System

In this paper, we propose a new software reliability growth model which is the mixture of two exponential reliability growth models, one of which has the reliability growth and the other one does not have the reliability growth after the software is released upon completion of testing phase. The mixture of two such models is characterized by a weighted factor p, which is the proportion of reliability growth part within the model. Firstly, this paper discusses an optimal software release problem with regard to the expected total software cost incurred during the warranty period under the proposed software reliability growth model, which generalizes Kimura, Toyota and Yamada's (1999) model with consideration of the weighted factor. The second main purpose of this paper is to apply the Bayesian approach to the optimal software release policy by assuming the prior distributions for the unknown parameters contained in the proposed software reliability growth model. Some numerical examples are presented for the purpose of comparing the optimal software release policies depending on the choice of parameters by the non-Bayesian and Bayesian methods.

Cost-reliability-optimal release policy for software reliability models incorporating improvements in testing efficiency

Over the past 30 years, many software reliability growth models (SRGMs) have been proposed for estimation of reliability growth of products during software development processes. One of the most important applications of SRGMs is to determine the software release time. Most software developers and managers always want to know the date on which the desired reliability goal will be met. In this paper, we first review a SRGM with generalized logistic testing-effort function and the proposed generalized logistic testing-effort function can be used to describe the actual consumption of resources during the software development process. Secondly, if software developers want to detect more faults in practice, it is advisable to introduce new test techniques, tools, or consultants, etc. Consequently, here we propose a software cost model that can be used to formulate realistic total software cost projects and discuss the optimal release policy based on cost and reliability considering testing effort and efficiency. Some theorems and several numerical illustrations are also presented. Based on the proposed models and methods, we can specifically address the problem of how to decide when to stop testing and when to release software for use.

A software cost model for quantifying the gain with considerations of random field environments

In this paper, we present a software gain model under random field environment with consideration of not only time to remove faults during in-house testing, cost of removing faults during beta testing, risk cost due to software failure, but also the benefits from reliable executions of the software during the beta testing and field operation. To our knowledge, this is the first study that incorporates the random field environmental factor into the cost model. We also provide an optimal release policy in which the net gain of the software development process is maximized. This gain model can help managers and developers to determine when to stop testing the software and release it to beta testing users and to end-users.

A stochastic dynamic programming model with fuzzy storage states for reservoir operations

Application of stochastic dynamic programming (SDP) models to reservoir optimization calls for state variables discretization. Reservoir storage volume is an important variable whose discretization has a pronounced effect on the computational efforts. The error caused by storage volume discretization is examined by considering it as a fuzzy state variable. In this approach, the point-to-point transitions between storage volumes at the beginning and end of each period are replaced by transitions between storage intervals. This is achieved by using fuzzy arithmetic operations with fuzzy numbers. In this approach, instead of aggregating single-valued crisp numbers, the membership functions of fuzzy numbers are combined. Running a simulation model with optimal release policies derived from fuzzy and non-fuzzy SDP models shows that a fuzzy SDP with a coarse discretization scheme performs as well as a classical SDP having much finer discretized space. It is believed that this advantage in the fuzzy SDP model is due to the smooth transitions between storage intervals which benefit from soft boundaries.

Systems of multiple cluster tools: configuration, reliability, and performance

The migration of semiconductor processes to single-wafer vacuum cluster tools has rendered configuration an important decision variable in fab operation and heightened the impact of reliability on fab performance. We address these closely linked issues by deriving the optimal configuration and operation of systems of cluster tools in the presence of scheduled maintenance. The two extremes in the spectrum of possible configurations are the serial configuration, in which the modules in a tool are all different, each representing a step in a process sequence, and the parallel configuration, in which each tool is assigned only one process step. We predict that the latter can offer higher throughputs. However, this advantage may be slight when equipment downtime is relatively schedulable and infrequent, in which the case the serial configuration may be preferable because of its superior cycle times. We also derive optimal lot sizing and release policies for systems of cluster tools. We conclude that fabs will gradually migrate from parallel configurations to serial as cluster tools become more reliable and cycle time becomes more important.

NHPP software reliability and cost models with testing coverage

This paper proposes a software reliability model that incorporates testing coverage information. Testing coverage is very important for both software developers and customers of software products. For developers, testing coverage information helps them to evaluate how much effort has been spent and how much more is needed. For customers, this information estimates the confidence of using the software product. Although research has been conducted and software reliability models have been developed, some practical issues have not been addressed. Testing coverage is one of these issues. The model is developed based on a nonhomogeneous Poisson process (NHPP) and can be used to estimate and predict the reliability of software products quantitatively. We examine the goodness-of-fit of this proposed model and present the results using several sets of software testing data. Comparisons of this model and other existing NHPP models are made. We find that the new model can provide a significant improved goodness-of-fit and estimation power. A software cost model incorporating testing coverage is also developed. Besides some traditional cost items such as testing cost and error removal cost, risk cost due to potential faults in the uncovered code is also included associated with the number of demands from customers. Optimal release policies that minimize the expected total cost subject to the reliability requirement are developed.

Minimizing variance of reservoir systems operations benefits using soft computing tools

Soft computing based tools including fuzzy inference systems (FIS), artificial neural networks (ANN), and genetic algorithms (GA) are used here to tackle the minimization of variance of benefits from reservoir operation. Variance reduction is a very hard optimization problem and solvable only using implicit methods like simulation, especially if the problem is nonlinear. First, a recently developed stochastic optimization method develops the optimal release policy (which is simply the recommended release in each season) of the system whose objective function maximizes the expected benefits. The policy is then simulated for a long inflow series to provide the trajectories of optimal releases and storages of the reservoir. These trajectories are then used as input-output data to train an adaptive neuro fuzzy inference system (ANFIS) to obtain updated fuzzy operating rules. The ANFIS based fuzzy rules are simulated and compared with policies developed using a multiple regression analysis, a commonly used method in water resources optimization. As the ANFIS performed better, further, a parameterized T-norm operator is applied and its parameters (numbering only two) are optimized through GA but with the objective of variance reduction in the benefits achieved. Results compare the better performance of the ANFIS based policies with other methods such as stochastic dynamic programming and the original stochastic method to demonstrate the usefulness of GA optimized parameters of a T-norm fuzzy operator for variance reduction.

Analysis of incorporating logistic testing-effort function into software reliability modeling

This paper investigates a SRGM (software reliability growth model) based on the NHPP (nonhomogeneous Poisson process) which incorporates a logistic testing-effort function. SRGM proposed in the literature consider the amount of testing-effort spent on software testing which can be depicted as an exponential curve, a Rayleigh curve, or a Weibull curve. However, it might not be appropriate to represent the consumption curve for testing-effort by one of those curves in some software development environments. Therefore, this paper shows that a logistic testing-effort function can be expressed as a software-development/test-effort curve and that it gives a good predictive capability based on real failure-data. Parameters are estimated, and experiments performed on actual test/debug data sets. Results from applications to a real data set are analyzed and compared with other existing models to show that the proposed model predicts better. In addition, an optimal software release policy for this model, based on cost-reliability criteria, is proposed.