In this article we test the accuracy of three platforms used in computational modelling: MatLab, Octave and Scilab, running on i386 architecture and three operating systems (Windows, Ubuntu and Mac OS). We submitted them to numerical tests using standard data sets and using the functions provided by each platform. A Monte Carlo study was conducted in some of the datasets in order to verify the stability of the results with respect to small departures from the original input. We propose a set of operations which include the computation of matrix determinants and eigenvalues, whose results are known. We also used data provided by NIST (National Institute of Standards and Technology), a protocol which includes the computation of basic univariate statistics (mean, standard deviation and first-lag correlation), linear regression and extremes of probability distributions. The assessment was made comparing the results computed by the platforms with certified values, that is, known results, computing the number of correct significant digits.

翻译：在本篇文章中,我们测试了计算模型中使用的三个平台的准确性:MatLab、Octave和Scilab,运行于i386建筑和三个操作系统(Windows、Ubuntu和Mac OS),我们用标准数据集和使用每个平台提供的功能进行了数字测试,对一些数据集进行了蒙特卡洛研究,以核实与原始输入小幅偏离有关的结果的稳定性。我们提出了一套操作,其中包括计算矩阵决定因素和精度值,其结果为已知。我们还使用了国家标准和技术研究所提供的数据,这是一个协议,其中包括计算基本单体统计数据(平均值、标准偏差和一lag相关性)、线性回归和概率分布的极端值。对平台计算的结果与验证值(已知结果,即计算正确重要数字的数量)进行比较。