Abstract. The Soboĺ sequence is the most popular quasirandom sequence because of its simplicity and efficiency in implementation. We summarize aspects of the scrambling technique applied to Soboĺ sequences and propose a new simpler modified scrambling algorithm, called the multi-digit scrambling scheme. Most proposed scrambling methods randomize a single digit at each iteration. In contrast, our multi-digit scrambling scheme randomizes one point at each iteration, and therefore is more efficient. After the scrambled Soboĺ sequence is produced, we use this sequence to evaluate a particular derivative security, and found that when this sequence is numerically tested, it is shown empirically to be far superior to the original unscrambled sequence.
In this paper, we design and expand the highly successful Information Assurance (IA) program through education and training opportunities in digital forensics for students in other disciplines, and for local law enforcement professionals. Faculty development efforts will focus on the formation of a rich, lab-based teaching environment for instruction and applied research in digital forensics technology. In this project, two academic departments, Computer & Information Sciences (CIS), and Sociology and Criminal Justice (SCJ), will develop a cross-disciplinary computer concentration in digital forensics that positions students for professional certification; this concentration is suitable for undergraduate students and law enforcement professionals.
Abstract. This paper presents work on generation of specificationdriven test data, by introducing techniques based on a subset of quasirandom sequences (completely uniformly distributed sequences) to generate test data. This approach is novel in software testing. This enhanced uniformity of quasirandom sequences leads to faster generation of test data covering all possibilities. We demonstrate by examples that welldistributed sequences can be a viable alternative to pseudorandom numbers in generating test data. In this paper, we present a method that can generate test data from a decision table specification more effectively via quasirandom numbers. Analysis of a simple problem in this paper shows that quasirandom sequences achieve better data than pseudorandom numbers, and have the potential to converge faster and so reduce the computational burden. Functional test coverage, an objective criteria, evaluates the quality of a test set to ensure that all specified behaviors will be exercised by the test data.
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