Determining the optimal allocation of testing resource for modular software system using dynamic programming

Abstract

Reliability is a major concern in the process of software development because unreliable software can cause failure in the computer system that can be hazardous. A way to enhance the reliability of software is to detect and remove the faults during the testing phase, which begins with module testing wherein modules are tested independently to remove a substantial number of faults within a limited resource. Therefore, the available resource must be allocated among the modules in such a way that the number of faults is removed as much as possible from each of the modules to achieve higher software reliability. In this article, we discuss the problem of optimal resource allocation of the testing resource for a modular software system, which maximizes the number of faults removed subject to the conditions that the amount of testing-effort is fixed, a certain percentage of faults is to be removed and a desired level of reliability is to be achieved. The problem is formulated as a non linear programming problem (NLPP), which is modeled by the inflection S-shaped software reliability growth models (SRGM) based on a non homogeneous Poisson process (NHPP) which incorporates the exponentiated Weibull (EW) testing-effort functions. A solution procedure is then developed using a dynamic programming technique to solve the NLPP. Furthermore, three special cases of optimum resource allocations are also discussed. Finally, numerical examples using three sets of software failure data are presented to illustrate the procedure developed and to validate the performance of the strategies proposed in this article. Experimental results indicate that the proposed strategies may be helpful to software project managers for making the best decisions in allocating the testing resource. In addition, the results are compared with those of Kapur et al. (2004), Huang and Lyu (2005), and Jha et al. (2010) that are available in the literature to deal the similar problems addressed in this article. It reveals that the proposed dynamic programming method for the testing-resource allocation problem yields a gain in efficiency over other methods.     

Structural inference of the parameters of the heteroscedastic simultaneous equation model

The structural approach of inference for the parameters of a simultaneous equation model with heteroscedastic error variance is investigated in this paper. The joint and the marginal structural distributions for the coefficients of the exogenous variables and the scale parameters of the error variables, and the marginal likelihood function of the coefficients of the endogenous variables have been derived. The estimates are directly obtainable from the structural distribution and the marginal likelihood function of the parameters. The marginal distribution of a subset of coefficients of exogenous variables provides the basis for making inference for a particular subset of parameter of interest.     

Maximum likelihood estimation of variance components of heteroscedastic random anova model

the estimation of variance components of heteroscedastic random model is discussed in this paper. Maximum Likelihood (ML) is described for one-way heteroscedastic random models. The proportionality condition that cell variance is proportional to the cell sample size, is used to eliminate the efffect of heteroscedasticity. The algebraic expressions of the estimators are obtained for the model. It is seen that the algebraic expressions of the estimators depend mainly on the inverse of the variance-covariance matrix of the observation vector. So, the variance-covariance matrix is obtained and the formulae for the inversions are given. A Monte Carlo study is conducted. Five different variance patterns with different numbers of cells are considered in this study. For each variance pattern, 1000 Monte Carlo samples are drawn. Then the Monte Carlo biases and Monte Carlo MSE’s of the estimators of variance components are calculated. In respect of both bias and MSE, the Maximum Likelihood (ML) estimators of variance components are found to be sufficiently good.     

Sexual and reproductive health knowledge and information-seeking behavior among middle eastern female university students: a systematic review

Objectives: The objective of this study was to collate the level of sexual and reproductive health knowledge, awareness, attitudes, and behavior among female university students, in addition to identifying their sources of information in Middle Eastern and North African countries. Methods: A systematic review was conducted with a search in Medline, EMBASE, Web of Knowledge, PsycINFO, and CINAHL from inception to February 2013. Included were studies in any language describing sexual and reproductive health knowledge and attitudes among female university students. Results: Nine studies were included, and all were cross-sectional surveys. There was a general lack of knowledge of sexual and reproductive health. The highest knowledge levels reported were for HIV. Conclusions: There are deficiencies in sexual and reproductive health knowledge and behavior in Middle Eastern and North African countries. Studies are needed to evaluate how to provide culturally sensitive sexual and reproductive health information.     

Designing stratified sampling in economic and business surveys

In most economic and business surveys, the target variables (e.g. turnover of enterprises, income of households, etc.) commonly resemble skewed distributions with many small and few large units. In such surveys, if a stratified sampling technique is used as a method of sampling and estimation, the convenient way of stratification such as the use of demographical variables (e.g. gender, socioeconomic class, geographical region, religion, ethnicity, etc.) or other natural criteria, which is widely practiced in economic surveys, may fail to form homogeneous strata and is not much useful in order to increase the precision of the estimates of variables of interest. In this paper, a stratified sampling design for economic surveys based on auxiliary information has been developed, which can be used for constructing optimum stratification and determining optimum sample allocation to maximize the precision in estimate.     

The challenges and coping strategies of single mothers caring for children with special needs: Experiences from Kashmir,India

Families play a significant role in the development of their children. Nearly 16% of children in the industrialized world live in single-parent households, which are mostly comprised of single mothers. Although motherhood is the most beautiful feeling a woman cherishes, this experience can be challenging when the woman has to raise their special-needs child on her own, without the support of her spouse or partner. This study aims to understand the challenges and coping strategies of single mothers while raising their special-needs children. To this end, in-depth case interviews were conducted with three single mothers selected purposively from the Srinagar area in Kashmir, India. The results revealed that multiple roles and responsibilities had an adverse effect on the psychological well-being of single mothers who had to deal with the financial burden of caring for their child's condition, including expenses for doctor visits, special therapies and medication, among other. In addition, they had to experience the social pressure and stigmatization attached to being a single mother. The most widely employed coping strategies include support from family and friends, religious faith, and self-coping. The study recommends the creation of welfare packages like free counseling, stipend support, inclusive education, free medical treatment, and therapies for Children with Special Needs (CWSNs) and their mothers.     

Working with Stepfamilies

Stepfamilies are complex family systems that warrant a specific model to guide therapy practice. Once a stepfamily has formed it's easy to overlook the lack of an attachment history that is commonly embedded in a biologically connected nuclear family. This can result in stepparents picking up parental responsibility for their stepchildren, which often may not go well. This paper highlights the need for clarity concerning the different levels of connection within a stepfamily, and the importance of avoiding ‘nuclear family‐style’ solutions and assumed attachments. This is especially important in the early stages of the relationship when everyone is adjusting to changing circumstances, which is often a time when issues of loyalty and betrayal fuel many of the actions taken. A range of family therapy techniques can be helpfully adapted to working with stepfamilies, especially as relationships with children often bring them to therapy. In particular, therapists can utilise ideas from structural family therapy to help guide the stepfamily to navigate the complexities of everyday life.     

A GQL estimation approach for analysing non-stationary over-dispersed BINAR(1) time series

This paper proposes a generalized quasi-likelihood (GQL) function for estimating the vector of regression and over-dispersion effects for the respective series in the bivariate integer-valued autoregressive process of order 1 (BINAR(1)) with Negative Binomial (NB) marginals. The auto-covariance function in the proposed GQL is computed using some ‘robust’ working structures. As for the BINAR(1) process, the inter-relation between the series is induced mainly by the correlated NB innovations that are subject to different levels of over-dispersion. The performance of the GQL approach is tested via some Monte-Carlo simulations under different combination of over-dispersion together with low and high serial- and cross-correlation parameters. The model is also applied to analyse a real-life series of day and night accidents in Mauritius.