Hoang Pham

A general imperfect-software-debugging model with S-shaped fault-detection rate

A general software reliability model based on the nonhomogeneous Poisson process (NHPP) is used to derive a model that integrates imperfect debugging with the learning phenomenon. Learning occurs if testing appears to improve dynamically in efficiency as one progresses through a testing phase. Learning usually manifests itself as a changing fault-detection rate. Published models and empirical data suggest that efficiency growth due to learning can follow many growth-curves, from linear to that described by the logistic function. On the other hand, some recent work indicates that in a real industrial resource-constrained environment, very little actual learning might occur because nonoperational profiles used to generate test and business models can prevent the learning. When that happens, the testing efficiency can still change when an explicit change in testing strategy occurs, or it can change as a result of the structural profile of the code under test and test-case ordering.

Reliability modeling of multi-state degraded systems with multi-competing failures and random shocks

In this paper, we develop a generalized multi-state degraded system reliability model subject to multiple competing failure processes, including two degradation processes, and random shocks. The operating condition of the multi-state systems is characterized by a finite number of states. We also present a methodology to generate the system states when there are multi-failure processes. The model can be used not only to determine the reliability of the degraded systems in the context of multi-state functions, but also to obtain the states of the systems by calculating the system state probabilities. Several numerical examples are given to illustrate the concepts.

A software cost model with warranty and risk costs

In this paper, a cost model with warranty cost, time to remove each error detected in the software system, and risk cost due to software failure is developed. A software reliability model based on non-homogeneous Poisson process is used. The optimal release policies to minimize the expected total software cost are discussed. A software tool is also developed using Excel and Visual Basic to facilitate the task of determining the optimal software release time. Numerical examples are provided to illustrate the results.

Software Reliability Assessment with OR Applications

Software Reliability Assessment with OR Applications is a comprehensive guide to software reliability measurement, prediction, and control. It provides a thorough understanding of the field and gives solutions to the decision-making problems that concern software developers, engineers, practitioners, scientists, and researchers. Using operations research techniques, readers will learn how to solve problems under constraints such as cost, budget and schedules to achieve the highest possible quality level.Software Reliability Assessment with OR Applications is a comprehensive text on software engineering and applied statistics, state-of-the art software reliability modeling, techniques and methods for reliability assessment, and related optimization problems. It addresses various topics, including:unification methodologies in software reliability assessment;application of neural networks to software reliability assessment;software reliability growth modeling using stochastic differential equations;software release time and resource allocation problems; andoptimum component selection and reliability analysis for fault tolerant systems.Software Reliability Assessment with OR Applications is designed to cater to the needs of software engineering practitioners, developers, security or risk managers, and statisticians. It can also be used as a textbook for advanced undergraduate or postgraduate courses in software reliability, industrial engineering, and operations research and management.

Considering fault removal efficiency in software reliability assessment

Software reliability growth models (SRGMs) have been developed to estimate software reliability measures such as the number of remaining faults, software failure rate, and software reliability. Issues such as imperfect debugging and the learning phenomenon of developers have been considered in these models. However, most SRGMs assume that faults detected during tests will eventually be removed. Consideration of fault removal efficiency in the existing models is limited. In practice, fault removal efficiency is usually imperfect. This paper aims to incorporate fault removal efficiency into software reliability assessment. Fault removal efficiency is a useful metric in software development practice and it helps developers to evaluate the debugging effectiveness and estimate the additional workload. In this paper, imperfect debugging is considered in the sense that new faults can be introduced into the software during debugging and the detected faults may not be removed completely. A model is proposed to integrate fault removal efficiency, failure rate, and fault introduction rate into software reliability assessment. In addition to traditional reliability measures, the proposed model can provide some useful metrics to help the development team make better decisions. Software testing data collected from real applications are utilized to illustrate the proposed model for both the descriptive and predictive power. The expected number of residual faults and software failure rate are also presented.

An inspection-maintenance model for systems with multiple competing processes

In some applications, the failure rate of the system depends not only on the time, but also upon the status of the system, such as vibration level, efficiency, number of random shocks on the system, etc., which causes degradation. In this paper, we develop a generalized condition-based maintenance model subject to multiple competing failure processes including two degradation processes, and random shocks. An average long-run maintenance cost rate function is derived based on the expressions for the degradation paths & cumulative shock damage, which are measurable. A geometric sequence is employed to develop the inter-inspection sequence. Upon inspection, one needs to decide whether to perform a maintenance, such as preventive or corrective, or to do nothing. The preventive maintenance thresholds for degradation processes & inspection sequences are the decision variables of the proposed model. We also present an algorithm based on the Nelder-Mead downhill simplex method to calculate the optimum policy that minimizes the average long-run maintenance cost rate. Numerical examples are given to illustrate the results using the optimization algorithm.

A Multi-Objective Optimization of Imperfect Preventive Maintenance Policy for Dependent Competing Risk Systems With Hidden Failure

This paper studies a multi-objective maintenance optimization embedded within the imperfect preventive maintenance (PM) for one single-unit system subject to the dependent competing risks of degradation wear and random shocks. We consider two kinds of random shocks in the system: 1) fatal shocks that will cause the system to fail immediately, and 2) nonfatal shocks that will increase the system degradation level by a certain cumulative shock amount. Also, an improvement factor in the form of quasi-renewal sequences is introduced to modulate the imperfect maintenance by raising the degradation critical threshold proportionally. Finally, the two decision variables for maintenance scheduling, the number of PMs to replacement, and the initial PM interval, are determined by simultaneously maximizing the system asymptotic availability, and minimizing the system cost rate using the fast elitist non-dominated Sorting Genetic Algorithm (NSGA-II). Sensitivity analysis for two parameters, including imperfect PM degree, and quasi-renewal coefficient of imperfect PM interval, is performed to provide insight into the behavior of the proposed maintenance policies. The comparison results show that the optimization solution is consistent between one-objective and multi-objective optimization, and the Pareto frontier for the maintenance optimization problem can provide alternative solutions according to customer preference and resource constraints.

On Recent Generalizations of the Weibull Distribution

This short communication first offers a clarification to a claim by Nadarajah & Kotz. We then present a short summary (by no means exhaustive) of some well-known, recent generations of Weibull-related lifetime models for quick information. A brief discussion on the properties of this general class is also given. Some future research directions on this topic are also discussed.

Springer handbook of engineering statistics

Springer handbook of engineering statistics , Springer handbook of engineering statistics , کتابخانه دیجیتال جندی شاپور اهواز

Modeling the Dependent Competing Risks With Multiple Degradation Processes and Random Shock Using Time-Varying Copulas

We develop s-dependent competing risk model for systems subject to multiple degradation processes and random shocks using time-varying copulas. The proposed model allows for a more flexible dependence structure between risks in which (a) the dependent relationship between random shocks and degradation processes is modulated by a time-scaled covariate factor, and (b) the dependent relationship among various degradation processes is fitted using the copula method. Two types of random shocks are considered in the model: fatal shocks, which fails the system immediately; and nonfatal shocks, which does not. In a nonfatal shock situation there are two impacts towards the degradation processes: sudden increment jumps, and degradation rate accelerations. The comparison results of the system reliability estimation from both constant and time-varying copulas are illustrated in the numerical examples to demonstrate the application of the proposed model. The modified joint distribution bounds in terms of Kendalls tau and Spearmans rho provide an improvement to Frechet-Hoeffding bounds for estimating the possible system reliability range.