Reliability growth models for hardware and software systems based on nonhomogeneous Poisson processes: A survey

In this paper we consider Markov analysis of models of complex software and hardware systems. A Markov analysis tool can be used during verification processes of models of avionics systems. In the introduction we enumerate main advantages and disadvantages of Markov analysis. For example, with Markov analysis, unlike other approaches, such as fault tree analysis and dependency diagram analysis, it is possible to analyze models of systems that are able to recovery. The main drawback of this approach is an exponential growth of models size with number of components in analyzed system. It makes Markov analysis barely used in practice. The other important problem is to develop a new algorithm for translating a model of a system to a model suitable for Markov analysis (Markov chain), since the existing solutions have significant limitations on the architecture of analyzed systems. Next we give a brief description of the context - AADL modeling language with Error Model Annex library, MASIW framework, and also give an explanation of Markov analysis method. In a main section we suggest an algorithm for translating a system model into a Markov chain, partially solving the problem of exponential growth of Markov chain. Then follows a description of further steps, and some heuristics that allow to extremely reduce running time of the algorithm. In this paper we also consider other Markov analysis tools and their features. As a result, we suggest a Markov analysis tool that can be effectively use in practice.

A non-homogeneous Poisson process with its mean value function generated by the cumulative distribution function of half logistic distribution is considered. It is modeled to assess the failure phenomenon of developed software. When the failure data are in the form of the number of failures in a given interval of time, the model parameters are estimated by the maximum likelihood method. The performance of the model is compared with two standard models [Goel, A. L., and Okumoto. K., “A Time Dependent Error Detection Rate Model for Software Reliability and Other Performance Measures,” IEEE Trans. Reliab., Vol. 28(3), 1979, pp. 206–211; Yamada , “S-Shaped Reliability Growth Modeling for Software Error Detection,” IEEE Trans. Reliab., Vol. 32(5), 1983, pp. 475–484] using two data sets. The release time of the software subject to a minimum expected cost is worked out and exemplified through illustrations.

The reliability of electronic circuits, systems and devices is one component of their quality. An electronic device has three phases of existence. First phase - the development is very important for reliability growth. In this phase constructor implements three basic components of reliability - reliability, longevity and maintainability in the electronic device. The reliability growth model (RGM) is a very specific tool used during the development phase of electronic devices and software products. New unconventional reliability growth models are based on the homogeneity testing of different Poisson process characteristics. This enables to find the time frame for finishing the technological constructing operations for further reliability growth in the next design of an electronic object. The goal is to find boundary time tb using statistical methods, from which the Poisson process is homogenous (stationary). We introduce experimental results those obtained by applying new unconventional reliability growth models. In the future it will be very interesting to develop algorithm testing the homogeneity of Poisson process of correlation function R (t1, t2), algorithm testing the homogeneity of Poisson process in his time-frequency representation. We review some new unconventional reliability growth models of electronic devices, compare and evaluate them in this paper.

The available state-based and path-based reliability models for the component-based software consume high computational cost and suffer from the evaluating complexity in the software evaluation. A SVM reliability evaluation model for component-based software systems (SVMREM) with a low complexity is presented in this paper. The component reliability SVM is performed to optimize the reliability evaluation time consumption. SVMREM has a low evaluation complexity and outperforms the state-based model and the path-based model of the reliability evaluation. Another advantage of SVMREM over the others is its software structure robustness. SVMREM only depends on the component historical reliabilities, which are independent from the software system structure. Based on the theory analysis and experiment results, it shows that the SVMREM reliability evaluating accuracy is acceptable when the software system structure is complex and the complexity of SVMREM is evidently lower than the contrast models.

The reliability growth models for software were studied. The MLE of the parameters for the exponential reliability growth model were given. The result shows that the Exponential model is useful for the data containing small samples. The model and the estimation method were applied to a data set. Compared with the Duane and AMSAA model, the estimators of the parameters and MTBF of ERGII has the precision to some extent. Furthermore, the model and the estimation of the parameters are applicable for small sample data.

Real-time process algebra (RTPA) is a denotational mathematical structure for denoting and manipulating system behavioral processes. RTPA is designed as a coherent algebraic system for intelligent and software system modeling, specification, refinement, and implementation. RTPA encompasses 17 metaprocesses and 17 relational process operations. RTPA can be used to describe both logical and physical models of software and intelligent systems. Logic views of system architectures and their physical platforms can be described using the same set of notations. When a system architecture is formally modeled, the static and dynamic behaviors performed on the architectural model can be specified by a three-level refinement scheme at the system, class, and object levels in a top-down approach. RTPA has been successfully applied in real-world system modeling and code generation for software systems, human cognitive processes, and intelligent systems.

This paper develops a discrete reliability growth (RG) model for an inverse sampling scheme, e.g., for destructive tests of expensive single-shot operations systems where design changes are made only and immediately after the occurrence of failures. For qi, the probability of failure at the i-th stage, a specific parametric form is chosen which conforms to the concept of the Duane (1964, IEEE Trans. Aerospace Electron. Systems, 2, 563-566) learning curve in the continuous-time RG setting. A generalized linear model approach is pursued which efficiently handles a certain non-standard situation arising in the study of large-sample properties of the maximum likelihood estimators (MLEs) of the parameters. Alternative closed-form estimators of the model parameters are proposed and compared with the MLEs through asymptotic efficiency as well as small and moderate sample size simulation studies.

This paper deals with the effect of interfailure time variability on the modelling of software reliability growth. Some, primarily mathematical arguments, are outlined that cast a doubt on the commonly adopted concept of failures ' occurrence as a Poisson (either homogeneous or non-homogeneous) process. On a contrived (but plausible for the software practice) example the consequences of this assumption, in some cases inappropriate for the software reliability predictions, are illustrated. The idea of “tuning” the reliability growth models based on measurements of the distribution of a single execution time is advocated.Keywordsurn modelsoftware reliability growthPoisson processsoftware execution timeoperational profile

It is one of the important issues in modern software development to make a highly reliable software system by efficient and economical testing. As one of these solutions, many companies have taken in testing-progress management and quality/reliability assessment based on software reliability growth models. Consequently, many software reliability growth models reflecting various development factors and operational environment of the software system have been proposed. Most of these models have assumed the software system as one domain. That is, the models have assumed that the set of the testing-paths influenced by execution of test-cases is extended with testing-progress and it expands to whole software system finally. However, a software system has the structure that the main-component calls plural sub-components ordinarily. Therefore, unless the main-component is tested, the sub-component called from it is not tested. In this chapter, we propose software reliability growth models based on such components' characteristics. Especially, these models reflecting the different testing-environment for composed components are formulated by nonhomogeneous Poisson processes. Furthermore, using fault-detection data observed in actual development projects, we show numerical examples of software reliability assessment and results of goodness-of-fit comparisons with existing software reliability growth models.

A new reliability growth model is presented which is simpler to plot and fits the data more closely than the Duane model over the range of Duane slopes normally observed during a reliability growth programme. The model is derived from variance stabilization transformation theory. The problem, inherent in Duane's model, of providing too much influence to earlier failures is overcome. A method is also presented of comparing different models which have different y-axes. Extensive simulations were conducted and the results indicate that for Duane slopes less than 0.5 the new model is more effective. The leverage and influence aspects of both models are evaluated by means of Cook's distance. Finally, two published datasets are analysed and the findings confirm the simulation results. Copyright © 1999 John Wiley & Sons, Ltd.

Software Engineering principles and practices promote the reuse of existing elements to maximize the performance of development and maintenance processes.The core concepts of reusability are aligned with the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles.From our perspective, FAIR principles are not only related to data in a software system, because there are other resources and parts of a system that are related to data indirectly or require data schemas and models for their description and management.Examples of these resources are services, business logic processes, contracts, security policies, architectures, components, deployment resources, execution rules, or interaction alternatives, among other possible examples.Actually, any resource in a system may need to be discovered either internally by a subprocess, or externally by any client that might be interested in finding, accessing, interoperating, or reusing it.This paper addresses a systematic literature review to analyze the current principles, paradigms, and technologies that can be applied to enable discovering and reusing mechanisms in modern software systems, and how traditional and outdated approaches have been adapted to support present issues.From this analysis, we outlined the challenges and open research lines that can be addressed to achieve a suitable reuse strategy.

Many buildings during their operational period incur damage of different origin: man-made, natural, operational, etc. Dynamic tests are performed for detailed assessment of the technical condition of buildings and structures in accordance with the regulatory documents for general analysis of the building damage state. In a large number of papers, the results of comparison of full-scale tests and numerical analysis using finite element method are presented. When analyzing the results, it can be concluded that the dynamic method is reliable, but has several limitations. The advantage of the dynamic method of building damage assessment is the possibility to adjust finite element models in software systems taking into account results obtained from in-situ tests, which allows to obtain more accurate results for the assessment of bearing capacity under seismic loading. To examine the effect of damage to buildings on their seismic resistance, an experiment with corrosiondamaged reinforced concrete columns was performed. The result of the first stage of the experiment is the assessment of the change in dynamic characteristics (eigenfrequency, vibration decrement, vibration damping coefficient, etc.) of reinforced concrete column specimens subjected to corrosion damage.

In this paper, we present three case studies of utility evaluations of underlying models in software systems: a user-model, technical and social models both singly and in combination, and a research-based model for user identification. Each of the three cases used a different approach to evaluating the model and each had challenges to overcome in designing and implementing the evaluation. We describe the methods we used and challenges faced in designing the evaluation procedures, summarize the lessons learned, enumerate considerations for those undertaking such evaluations, and present directions for future work.

The paper investigates the evaluating task of the traffic as the registered number of vehicles of one of the three classes (a bus, passenger car or truck) that passed in a certain direction per unit of time, according to data from CCTV cameras in real time. For simplification this problem solving was divided into three components. They are registration of traffic flow objects and their classification, finding trajectories of fixed objects and determining traffic lanes, as well as counting objects in each direction. A comparative analysis of the existing algorithms, software and hardware systems that solve problems similar to the problems was carried out; also, a proprietary approach was proposed. The analysis describes the feasibility for developing their own approach due to the lack of an algorithm that meets all the requirements, such as estimating the traffic on the basis of data from surveillance cameras. Besides, the existing hardware and software systems are of the high cost and complexity. The YOLO artificial neural network was used to recognize objects on the frame in the form of rectangles containing the image of a vehicle. To build tracks, objects are compared frame-by-frame based on a set of characteristics, including a simple (average) perceptual hash and the coordinates of the corresponding rectangle. The developed algorithm for constructing trajectories of moving traffic objects, highlighting lanes of automobile traffic and counting vehicles is proposed as a software module. The testing results concerning the quality of the algorithm are introduced. The main advantages and disadvantages of the proposed approach are highlighted.

This paper describes a convenient alternative to traditional reliability growth models. This new reliability growth model utilizes Bayesian statistics. Equations for estimating MTBF, /spl theta/, or failure rate, /spl lambda/, and its confidence limits were derived by assuming the prior density function of I//spl theta/ is gamma. These equations can be used to periodically estimate MTBF and its confidence limit. A smooth growth curve can be obtained by best-fitting a function through the estimated points. This reliability growth model provides a simple and efficient tool to evaluate reliability growth of a system.