Search Results for "model based testing tool"

An EDM calibration baseline is used to derive specific instrument errors of the electronic distance measurement (EDM) unit of a total station. EDMFit is a simple adjustment tool to estimate the EDM calibration parameters (addition constant and/or scale parameter). The adjustment process is according to ISO 17123-4. The parameters are checked for significance, to avoid an overestimation. Based on two classical hypothesis testing, EDMFit provides a reliable outlier detection. For uncertainties budgeting, EDMFit makes use of Monte-Carlo simulations and supports different distribution function: - Uniform distribution, - Triangular distribution and - Gaussian (normal) distribution. ...

JBernstein is a tool implemented in Java that enables to check whether a polynomial constraint holds for all variables within their domain. It is mainly used in the verification or synthesis of cyber-physical systems where the system model is nonlinear. The tool also supports specifications in assume-guarantee style. The underlying algorithm is based on a translation to Bernstein polynomials, and several optimization techniques are implemented to ensure efficiency. ...

DeDAY (Demography Data Analyses) is a tool of analyzing demography data. It supports Gompertz, Weibull and Logistic distributions. DeDay also supports mixed mortality models based on these distribution such as the Gompertz-Makeham distribution. Distributions such as Gompertz describes only age-dependent mortality, which increases over time. Mixed mortality models, such as in Gompertz-Makeham distribution, consider a more general case where mortality is consist of both age-dependent and in-dependent mortality. ...

Automata Based Programming Tool for implementing logic for J2SE, J2EE and Sybmian applications. Consists of Java FSM Framework and Eclipse Plugin parts. Plugin implements UML model editor, visual debugger, validator, compiler and interpreter for Eclipse

PlexBench is a cross-platform, web-enabled, analysis tool that is driven by a scalable backpropagation feed-forward neural network. It uses embedded Perl for scripting and is written in the style of an in-process Component Object Model (COM) C++ program.