N-version programming has been proposed as a method of incorporating fault tolerance into software. Multiple versions of a program (i.e. "N") are prepared and executed in parallel. Their outputs are collected and examined by a voter,and, if they are not identical, it is assumed that the majority is correct. This method depends for its reliability improvement on the assumption that programs that have been developed independently will fail independently. In this paper an experiment is described in which the fundamental axiom is tested. A total of twenty seven versions of a program were prepared independently from the same specification at twouniversities and then subjected to one million tests. The results of the tests revealed that the programs were individually extremely reliable but that the number of tests in which more than one program failed was substantially more than expected. The results of these tests are presented along with an analysis of some of the faults that were found in the programs. Background information on the programmers used is also summarized. The conclusion from this experiment is that N-version programming must be used with care and that analysis of its reliability must include the effect of dependent errors. Keywords and Phrases: Multi-version programming, N-version programming, software reliability,fault-tolerant software, design diversity.

set of measurements from a large outdoor routing experiment, we start by evaluating the relative realism of common assumptions made in radio model design, identifying those which provide a reasonable approximation of reality. Although several such investigations have been made for static sensor

philip.resnikfleast.sun.com This paper presents a new measure of semantic similarity in an IS-A taxonomy, based on the notion of information content. Experimental evaluation suggests that the measure performs encouragingly well (a correlation of r = 0.79 with a benchmark set of human similarity

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This paper describes a database designed to evaluate the performance of speech recognition algorithms in noisy conditions. The database may either be used to measure frontend feature extraction algorithms, using a defined HMM recognition back-end, or complete recognition systems. The source speech

This article presents a measure of semantic similarityinanis-a taxonomy based on the notion of shared information content. Experimental evaluation against a benchmark set of human similarity judgments demonstrates that the measure performs better than the traditional edge-counting approach

suite that has been designed to fill this gap. This suite has been constructed through a three-step process: intuition and market driven initial selection, experimental measurement to establish uniqueness, and integration with system synthesis algorithms to establish usefulness.

-diversity that can defend against such attacks. In addition to building a formal foundation for ℓ-diversity, we show in an experimental evaluation that ℓ-diversity is practical and can be implemented efficiently.

clusters of arbitrary shape. DBSCAN requires only one input parameter and supports the user in determining an appropriate value for it. We performed an experimental evaluation of the effectiveness and efficiency of DBSCAN using synthetic data and real data of the SEQUOIA 2000 benchmark. The results of our

of objects and split management, whF h keep th M-tree always balanced - severalheralvFV split alternatives are considered and experimentally evaluated. Algorithd for similarity (range and k-nearest neigh bors) queries are also described. Results from extensive experimentationwith a prototype system