A discrete time model for software reliability with application to a flight control software

This work is motivated by a particular software reliability problem in a unit of flight control software developed by the Indian Space Research Organization (ISRO), in which the testing of the software is carried out in multiple batches, each consisting of several runs. As the errors are found during the runs within a batch, they are noted, but not debugged immediately; they are debugged only at the end of that particular batch of runs. In this work, we introduce a discrete time model suitable for this type of periodic debugging schedule and describe maximum likelihood estimation for the model parameters. This model is used to estimate the reliability of the software. We also develop a method to determine the additional number of error‐free test runs required for the estimated reliability to achieve a specific target with some high probability. We analyze the test data on the flight control software of ISRO. Copyright © 2011 John Wiley & Sons, Ltd.     

A reliability assessment tool for distributed software development environment based on Java and J/Link

In this paper, we propose a software reliability assessment tool based on software reliability growth models for distributed development environment by using Java programming language and J/Link technology in Mathematica. Java is an object-oriented and platform-free programming language which has several advantages such as modularization and reusability of the existing code. Especially J/Link can use and control the Mathematica kernel from a Java program. By using this J/Link technology, we can reduce some efforts to develop the tool because of reusing the existing Mathematica code used for the research. Our tool proposed here is useful for software developers in terms of the management of the system-testing process in distributed development environment.     

Risk analysis techniques and their application to software development

The past decade has seen a dramatic change in the emphasis of software to hardware. Whereas a project may have comprised 80% hardware and 20% software, the reverse is now generally more realistic. This has resulted in a great deal of interest focusing around the areas of quality metrics and reliability growth applied to the software lifecycle. Risk analysis has to date seen many applications in the assessment of hardware but little in the software area. This paper reviews the risk analysis techniques that have been developed in a range of industries. Progress in this area has been most apparent in the chemical and nuclear power industries where probabilistic risk assessment has been used to estimate the total risk associated with the whole plant. A discussion of the appropriateness of the techniques to software safety assessment is included together with an outline of the methods currently being used to detect software faults. Suggestions are made for a new methodology in analysing software safety.     

Mixed poisson-type processes with application in software reliability

We introduce one generalization of the mixed Poisson process referred to as the mixed Poisson-type process. An approach taken here is to assume the l₁-isotropy of interevent times and to define the parameter as a function of observable quantities. An inhomogeneous variant of the new process is studied as a software reliability model. As an illustration a numerical example is analyzed via the Gibbs sampler. The mixed Poisson-type process is constructed through probabilistic behaviour of observable quantities and includes the mixed Poisson process as the limiting case.     

A methodology for priority setting with application to software development process

In this paper we briefly review the basic ideas behind the Analytic Hierarchy Process (AHP). Based on these ideas, we introduce the concept of comparison interval and propose a methodology based on stochastic optimization to achieve global consistency and to accommodate the fuzzy nature of the comparison process. We also discuss systems with feedback. For systems with inter-component dependence, we review the super-matrix technique. For systems with intra-component dependence, we propose a systematic approximation scheme to compute the weight vector for priority setting. Applications of software development process and a numerical example are given to illustrate our methodology and results.     

Inference for nonstationary time series of counts with application to change-point problems

Annals of the Institute of Statistical Mathematics - We consider an integer-valued time series $$(Y_t)_{t\in {\mathbb {Z}}}$$ where the model after a time $$k^*$$ is Poisson autoregressive with the...     

Enhancing software reliability modeling and prediction through the introduction of time-variable fault reduction factor

Over the past three decades, many software reliability models with different parameters, reflecting various testing characteristics, have been proposed for estimating the reliability growth of software products. We have noticed that one of the most important parameters controlling software reliability growth is the fault reduction factor (FRF) proposed by Musa. FRF is generally defined as the ratio of net fault reduction to failures experienced. During the software testing process, FRF could be influenced by many environmental factors, such as imperfect debugging, debugging time lag, etc. Thus, in this paper, we first analyze some real data to observe the trends of FRF, and consider FRF to be a time-variable function. We further study how to integrate time-variable FRF into software reliability growth modeling. Some experimental results show that the proposed models can improve the accuracy of software reliability estimation. Finally, sensitivity analyses of various optimal release times based on cost and reliability requirements are discussed. The analytic results indicate that adjusting the value of FRF may affect the release time as well as the development cost.     

An imperfect debugging model with two types of hazard rates for software reliability measurement and assessment

We propose a software reliability model which assumes that there are two types of software failures. The first type is caused by the faults latent in the system before the testing; the second type is caused by the faults regenerated randomly during the testing phase. The former and latter software failure-occurrence phenomena are described by a geometrically decreasing and a constant hazard rate, respectively. Further, this model describes the imperfect debugging environment in which the fault-correction activity corresponding to each software failure is not always performed perfectly. Defining a random variable representing the cumulative number of faults successfully corrected up to a specified time point, we use a Markov process to formulate this model. Several quantitative measures for software reliability assessment are derived from this model. Finally, numerical examples of software reliability analysis based on the actual testing data are presented.     

Determining attribute weights to improve solution reliability and its application to selecting leading industries

In multiple attribute decision analysis, many methods have been proposed to determine attribute weights. However, solution reliability is rarely considered in those methods. This paper develops an objective method in the context of the evidential reasoning approach to determine attribute weights which achieve high solution reliability. Firstly, the minimal satisfaction indicator of each alternative on each attribute is constructed using the performance data of each alternative. Secondly, the concept of superior intensity of an alternative is introduced and constructed using the minimal satisfaction of each alternative. Thirdly, the concept of solution reliability on each attribute is defined as the ordered weighted averaging (OWA) of the superior intensity of each alternative. Fourthly, to calculate the solution reliability on each attribute, the methods for determining the weights of the OWA operator are developed based on the minimax disparity method. Then, each attribute weight is calculated by letting it be proportional to the solution reliability on that attribute. A problem of selecting leading industries is investigated to demonstrate the applicability and validity of the proposed method. Finally, the proposed method is compared with other four methods using the problem, which demonstrates the high solution reliability of the proposed method.     

Exploring the latent segmentation space for the assessment of multiple change-point models

This paper addresses the retrospective or off-line multiple change-point detection problem. Multiple change-point models are here viewed as latent structure models and the focus is on inference concerning the latent segmentation space. Methods for exploring the space of possible segmentations of a sequence for a fixed number of change points may be divided into two categories: (i) enumeration of segmentations, (ii) summary of the possible segmentations in change-point or segment profiles. Concerning the first category, a dynamic programming algorithm for computing the top $N$ most probable segmentations is derived. Concerning the second category, a forward-backward dynamic programming algorithm and a smoothing-type forward-backward algorithm for computing two types of change-point and segment profiles are derived. The proposed methods are mainly useful for exploring the segmentation space for successive numbers of change points and provide a set of assessment tools for multiple change-point models that can be applied both in a non-Bayesian and a Bayesian framework. We show using examples that the proposed methods may help to compare alternative multiple change-point models (e.g. Gaussian model with piecewise constant variances or global variance), predict supplementary change points, highlight overestimation of the number of change points and summarize the uncertainty concerning the position of change points.     

A systematic approach to parameter optimization and its application to flight schedule simulation software

Journal of Heuristics - Industrial software often has many parameters that critically impact performance. Frequently, these are left in a sub-optimal configuration for a given application because...     

A markov process model for software reliability analysis

Software reliability is a rapidly developing discipline. In this paper we model the fault-detecting processes by Markov processes with decreasing jump intensity. The intensity function is suggested to be a power function of the number of the remaining faults in the software. The models generalize the software reliability model suggested by Jelinski and Moranda (‘Software reliability research’, in W. Freiberger (ed.), Statistical Computer Performance Evaluation, Academic Press, New York, 1972. pp. 465–497). The main advantage of our models is that we do not use the assumption that all software faults correspond to the same failure rate. Preliminary studies suggest that a second-order power function is quite a good approximation. Statistical tests also indicate that this may be the case. Numerical results show that the estimation of the expected time to next failure is both reasonable and decreases relatively stably when the number of removed faults is increased.     

A Markov modulated Poisson model for software reliability

In this paper, we consider a latent Markov process governing the intensity rate of a Poisson process model for software failures. The latent process enables us to infer performance of the debugging operations over time and allows us to deal with the imperfect debugging scenario. We develop the Bayesian inference for the model and also introduce a method to infer the unknown dimension of the Markov process. We illustrate the implementation of our model and the Bayesian approach by using actual software failure data.     

Statistical software reliability prediction and its applicability based on mean time between failures

One of the most important issues for a development manager may be how to predict the reliability of a software system at an arbitrary testing time. In this paper, using the software failure-occurrence time data, we discuss a method of software reliability prediction based on software reliability growth models described by an NHPP (nonhomogeneous Poisson process). From the applied software reliability growth models, the conditional probability distribution of the time between software failures is derived, and its mean and median are obtained as reliability prediction measures. Finally, based on several numerical examples, we compare the performance between these measures from the view point of software reliability prediction in the testing phase.     

Power processes and their application to reliability

We introduce the stochastic process we call a power process and show that under certain conditions it is stochastically monotone. We use it to model a deteriorative system where operating times and repair times are general discrete random variables, and illustrate our results with numerical examples.     

多项选择题测验信度的一个估计方法及其应用

在多项选择题测验分数等于真实分数和猜测分数之和的假设模型下，本文得到了一个多项选择题测验信度的理论公式，并由此给出了测验信度的估计方法。最后，通过两个例子，说明了本文提出的方法在测验信度分析中的应用，并将这种方法与教育测量中常用的库德——理查逊方法（Ｋｕｄｅｒ－Ｒｉｃｈａｒｄｓｏｎ）进行了比较     

Continuous model for software reliability demonstration testing considering damage size of software failures

This study proposes a continuous model for software reliability demonstration testing, which takes the damage size of software failures into consideration. In the proposed procedure, the software being tested is accepted if the number of software failures does not exceed a prespecified value, s and if the total damage size of software failures is less than another prespecified value, d. Based on the producer's and consumer's risks, an algorithm for designing a software reliability demonstration test is discussed.     

Effect of change point and imperfect debugging in software reliability and its optimal release policy

This article presents a software reliability growth model based on non-homogeneous Poisson process. The main focus of this article is to deliver a method for software reliability modelling incorporating the concept of time-dependent fault introduction and fault removal rate with change point. Also in this article, a cost model with change point has been developed. Based on the cost model optimal release policy with change point has been discussed. Maximum likelihood technique has been applied to estimate the parameters of the model. The proposed model has been validated using some real software failure data. Comparison has been made with models incorporating change point and without change point. The application of the proposed cost model has been shown using some numerical examples.     

Adaptive reliability analysis based on a support vector machine and its application to rock engineering

The response surface method (RSM), a simple and effective approximation technique, is widely used for reliability analysis in civil engineering. However, the traditional RSM needs a considerable number of samples and is computationally intensive and time-consuming for practical engineering problems with many variables. To overcome these problems, this study proposes a new approach that samples experimental points based on the difference between the last two trial design points. This new method constructs the response surface using a support vector machine (SVM); the SVM can build complex, nonlinear relations between random variables and approximate the performance function using fewer experimental points. This approach can reduce the number of experimental points and improve the efficiency and accuracy of reliability analysis. The advantages of the proposed method were verified using four examples involving random variables with different distributions and correlation structures. The results show that this approach can obtain the design point and reliability index with fewer experimental points and better accuracy. The proposed method was also employed to assess the reliability of a numerically modeled tunnel. The results indicate that this new method is applicable to practical, complex engineering problems such as rock engineering problems.