International Journal of Reliability, Quality and Safety Engineering

Most of software reliability growth models (SRGMs) describe a software reliability growth process depending on only testing-time. However, it is said that a software reliability growth process in an actual testing-phase of a software development process depends on not only testing-time but also testing-effort factors. And we often observe a phenomenon that stochastic characteristics of the software failure-occurrence time or the software failure-occurrence time-interval changes notably in an actual testing-phase. The testing-time when such phenomenon is observed is called change-point. It is said that the effect of change-point on the software reliability growth process influences accuracy for software reliability assessment based on conventional SRGMs. This paper discusses a two-dimensional software reliability growth modeling with change-point for describing an actual phenomenon being related to the software reliability growth process. Further, we show examples of the applications of software reliability assessment based on our two-dimensional SRGM by using actual data.

Structural components in aircraft are often required to be operative beyond their original design service objectives (DSO). Vital issues for aging infrastructures are estimation and prediction, with confidence, of residual life, reliability, and availability, given the service history. Uncertainty increasingly is considered to be a major factor. Demanding high reliability exacerbates the role of uncertainty. One aspect of life cycle management for aging aircraft was investigated by replicating laboratory specimens subjected to fatigue loading that is typical for a class of military aircraft wing skins. Samples were fabricated from 7075-T6 plate aluminum alloy similar to that used for wing panels. A total of 15 specimens were tested. Tests were terminated when the fatigue life expended (FLE) reached a prescribed value of 100%, 150%, or 200% of the component DSO. Then, microscopy was used to quantify the size of fatigue cracks within high stress regions of simulated fastener holes in laboratory specimens. Cumulative distribution functions (cdfs) for geometrical properties of cracks and constituent particles in the alloy were characterized statistically as input for residual life estimations and for life cycle management analyses. Insights into crack initiation and growth are also provided.

Fatigue life corresponding to specific failure probability is much more significant than mean life, since fatigue fracture is a highly uncertain phenomenon. For a complex structural component with several damage sites, the contributions of the every damage site are stochastic in nature. Moreover, the damages or fatigue lives of the individual vulnerable sites are neither completely correlated nor perfectly independent of each other. The present paper develops a probabilistic fatigue life evaluation approach to predict the probabilistic fatigue life of complex mechanical or structural components containing multiple damage sites. By correctly dealing with the failure dependence of the every damage sites and characterizing the fatigue life random variable of a multi-site-damage (MSD) structure or component using the extreme statistic of the fatigue lives of the individual damage sites, probabilistic fatigue life of MSD structural component is reasonably predicted.

Appropriate model assumption and robust model estimation are critical for accurate hard disk drive (HDD) field return prediction. Parametric distribution models that are conventionally used in HDD reliability analyses do not describe well HDD field survival. A biological growth model, Bertalanfy-Richards growth model, is introduced in this paper to model HDD field survival within warranty period. A nonparametric multiple missing value imputation approach is used to improve model estimation when not enough observations of field returns are available at early stage of product life. Tests of the model and the missing value imputation approach using actual HDD field survival data suggest they perform well in describing and predicting HDD warranty field returns.

Survey analysis method is widely used in many areas such as social study, marketing research, economics, public health, clinical trials and transportation data analysis. Minimum sample size determination is always needed before a survey is conducted to avoid huge cost. Some statistical methods can be found from the literature for finding the minimum required sample size. This paper proposes a method for finding the minimum total sample size needed for the survey when the population is divided into cells. The proposed method can be used for both the infinite population case and the finite population case. A computer program is needed to realize the sample size calculation. The computer program the authors used is SAS/IML, which is a special integrated matrix language (IML) procedure of the Statistical Analysis System (SAS) software.

This paper provides a preliminary investigation into the application of the Maximum Entropy Principle (MEP), introduced by Jaynes in 1957, in modeling discrete time Software Reliability Growth Model (SRGM). On their own, each of these two topics are interesting with extensive applications, and here we will show how they can be combined to provide yet another application of the MEP among a huge array of proven successful applications. A brief discussion of MEP and SRGM will be given and a hitherto unnoticed relationship between MEP distribution and the Lagrange interpolation polynomials highlighted. We then show how MEP can be used to obtain some important distributions arising from discrete time SRGM. Finally, a simple example is given to illustrate the theory.