Randomized Quasi-Random Testing

Random testing is a fundamental testing technique that can be used to generate test cases for both hardware and software systems. Quasi-random testing was proposed as an enhancement to the cost-effectiveness of random testing: In addition to having similar computation overheads to random testing, it makes use of quasi-random sequences to generate low-discrepancy and low-dispersion test cases that help deliver high failure-detection effectiveness. Currently, few algorithms exist to generate quasi-random sequences, and these are mostly deterministic, rather than random. A previous study of quasi-random testing has examined two methods for randomizing quasi-random sequences to improve their applicability in testing. However, these randomization methods still have shortcomings - one method does not introduce much randomness to the test cases, while the other does not support incremental test case generation. In this paper, we present an innovative approach to incrementally randomizing quasi-random sequences. The test cases generated by this new approach show a high degree of randomness and evenness in distribution. We also conduct simulations and empirical studies to demonstrate the applicability and effectiveness of our approach in software testing

Testing When a Parameter Is on the Boundary of the Maintained Hypothesis

This paper considers testing problems where several of the standard regularity conditions fail to hold. We consider the case where (i) parameter vectors in the null hypothesis may lie on the boundary of the maintained hypothesis and (ii) there may be a nuisance parameter that appears under the alternative hypothesis, but not under the null. The paper establishes the asymptotic null and local alternative distributions of quasi-likelihood ratio, rescaled quasi-likelihood ratio, Wald, and score tests in this case. The results apply to tests based on a wide variety of extremum estimators and apply to a wide variety of models. Examples treated in the paper are: (1) tests of the null hypothesis of no conditional heteroskedasticity in a GARCH(1, 1) regression model and (2) tests of the null hypothesis that some random coefficients have variances equal to zero in a random coefficients regression model with (possibly) correlated random coefficients.Asymptotic distribution, boundary, conditional heteroskedasticity, extremum estimator, GARCH model, inequality restrictions, likelihood ratio test, local power, maximum likelihood estimator, parameter restrictions, random coefficients regression, quasi-maximum likelihood estimator, quasi-likelihood ratio test, restricted estimator, score test, Wald test

Quasi-Random resamplings, with applications to rule-samplng, cross-validation and (su-)bagging

Resampling (typically, but not necessarily, bootstrapping) is a well-known stochastic technique for improving estimates in particular for small samples. It is known very efficient in many cases. Its drawback is that resampling leads to a compromise computational cost / stability through the number of resamplings. The computational cost is due to the study of multiple randomly drawn resam- ples. Intuitively, we want some more properly distributed resamples to improve the stability of resampling-based algorithms. Quasi-random numbers are a well- known technique for improving the convergence rate of data-based estimates. We here consider quasi-random version of resamplings. We apply this technique to BSFD, a data-mining algorithm for simultaneous-hypothesis-testing, to cross- validation, and to (su-)bagging, an ensemble method for learning. We present quasi-random numbers in section 2. We present bootstrap and a quasi-random version of bootstrap-sampling in section 3. We present experimental results in section 4

Maintaining symmetry of simulated likelihood functions

This paper suggests solutions to two different types of simulation errors related to Quasi-Monte Carlo integration. Likelihood functions which depend on standard deviations of mixed parameters are symmetric in nature. This paper shows that antithetic draws preserve this symmetry and thereby improves precision substantially. Another source of error is that models testing away mixing dimensions must replicate the relevant dimensions of the quasi-random draws in the simulation of the restricted likelihood. These simulation errors are ignored in the standard estimation procedures used today and this paper shows that the result may be substantial estimation- and inference errors within the span of draws typically applied.Quasi-Monte Carlo integration; Antithetic draws; Likelihood Ratio tests; simulated likelihood; panel mixed multinomial logit; Halton draws

Random Intertemporal Choice

We provide a theory of random intertemporal choice. Agents exhibit stochastic choice over consumption due to preference shocks to discounting attitudes. We first demonstrate how the distribution of these preference shocks can be uniquely identified from random choice data. We then provide axiomatic characterizations of some common random discounting models, including exponential and quasi-hyperbolic discounting. In particular, we show how testing for exponential discounting under stochastic choice involves checking for both a stochastic version of stationarity and a novel axiom characterizing decreasing impatience

Approximate flow friction factor: Estimation of the accuracy using Sobol’s Quasi-Random sampling

The unknown friction factor from the implicit Colebrook equation cannot be expressed explicitly in an analytical way, and therefore to simplify the calculation, many explicit approximations can be used instead. The accuracy of such approximations should be evaluated only throughout the domain of interest in engineering practice where the number of test points can be chosen in many different ways, using uniform, quasi-uniform, random, and quasi-random patterns. To avoid picking points with undetected errors, a sufficient minimal number of such points should be chosen, and they should be distributed using proper patterns. A properly chosen pattern can minimize the required number of testing points that are sufficient to detect maximums of the error. The ability of the Sobol quasi-random vs. random distribution of testing points to capture the maximal relative error using a sufficiently small number of samples is evaluated. Sobol testing points that are quasi-randomly distributed can cover the domain of interest more evenly, avoiding large gaps. Sobol sequences are quasi-random and are always the same, which allows the exact repetition of scientific results

The design of OpticalGamification (OG) with random model in learning interference and diffraction

This article describes the design and preliminary field testing of using a gamification-application with random model in the learning process of interference and diffraction topics for pre-service physics teachers (PPT). The gamification-application in this research is called OpticalGamification (OG) featuring random model. This research is a quasi-experimental research with a time-series design involving 34 PPT at a university in the city of Jakarta, Indonesia. Data related to the PPT’ concept mastery are collected through test instruments in the form of 50 questions which are an integration of multiple-choice questions, reasoned multiple-choice questions, and essays. This research resulted in a product called OG with random model with several features, including profiles, gamification, forums, achievement pages, projects and leaderboard. The result of preliminary field testing of using the OG with random model shows that the PPT’ concept mastery has increased from series 1 to the next following series

Generalized Minimum Penalized Hellinger Distance Estimation and Generalized Penalized Hellinger Deviance Testing for Generalized Linear Models: The Discrete Case

In this dissertation, robust and efficient alternatives to quasi-likelihood estimation and likelihood ratio tests are developed for discrete generalized linear models. The estimation method considered is a penalized minimum Hellinger distance procedure that generalizes a procedure developed by Harris and Basu for estimating parameters of a single discrete probability distribution from a random sample. A bootstrap algorithm is proposed to select the weight of the penalty term. Simulations are carried out to compare the new estimators with quasi-likelihood estimation. The robustness of the estimation procedure is demonstrated by simulation work and by Hapel\u27s α-influence curve. Penalized minimum Hellinger deviance tests for goodness-of-fit and for testing nested linear hypotheses are proposed and simulated. A nonparametric bootstrap algorithm is proposed to obtain critical values for the testing procedure

Quasi-Monte Carlo Methods in Cash Flow Testing Simulations

What actuaries call cash flow testing is a large-scale simulation pitting a company\u27\u27s current policy obligation against future earnings based on interest rates. While life contingency issues associated with contract payoff are a mainstay of the actuarial sciences, modeling the random fluctuations of US Treasury rates is less studied. Furthermore, applying standard simulation techniques, such as the Monte Carlo method, to actual multi-billion dollar companies produce a simulation that can be computationally prohibitive. In practice, only hundreds of sample paths can be considered, not the usual hundreds of thousands one might expect for a simulation of this complexity. Hence, insurance companies have a desire to accelerate the convergence of the estimation procedure. The paper reports the results of cash flow testing simulations performed for Conseco L.L.C. using so-called quasi-Monte Carlo techniques. In these, pseudo-random number generation is replaced with deterministic low discrepancy sequences. It was found that by judicious choice of subsequences, that the quasi-Monte Carlo method provided a consistently tighter estimate than the traditional methods for a fixed, small number of sample paths. The techniques used to select these subsequences are discussed

КВАЗИСЛУЧАЙНОЕ ТЕСТИРОВАНИЕ ВЫЧИСЛИТЕЛЬНЫХ СИСТЕМ

Various modified random testing approaches have been proposed for computer system testing in the black box environment. Their effectiveness has been evaluated on the typical failure patterns by employing three measures, namely, P-measure, E-measure and F-measure. A quasi-random testing, being a modified version of the random testing, has been proposed and analyzed. The quasi-random Sobol sequences and modified Sobol sequences are used as the test patterns. Some new methods for Sobol sequence generation have been proposed and analyzed.Анализируются причинно-следственные связи при возникновении неисправностей вычисли-тельных систем. Даются определения понятий «неисправность», «ошибка» и «неисправное поведение вычислительных систем», показывается их общность для программной и аппаратной частей вычислительных систем. Рассматривается классификация обобщенных входных тестовых воздей-ствий на три категории: точечные тестовые наборы, узкополосные тестовые наборы и блочные тестовые наборы. Приводится анализ методов тестирования вычислительных систем по методике черного ящика, показывается эффективность использования квазислучайного тестирования. Анали-зируются и предлагаются методы формирования квазислучайных тестовых воздействий