Search results
Results from the WOW.Com Content Network
The models have two basic types - prediction modeling and estimation modeling. 1.0 Overview of Software Reliability Prediction Models. These models are derived from actual historical data from real software projects. The user answers a list of questions which calibrate the historical data to yield a software reliability prediction.
RAMP Simulation Software for Modelling Reliability, Availability and Maintainability (RAM) is a computer software application developed by WS Atkins specifically for the assessment of the reliability, availability, maintainability and productivity characteristics of complex systems that would otherwise prove too difficult, cost too much or take too long to study analytically.
SAS, [30] a system of software products for statistics. It includes SAS/IML, [31] a matrix programming language. VisSim is a visual block-diagram language for simulation of nonlinear dynamic systems and model-based embedded development. Its fast ODE engine supports real-time simulation of complex large-scale models.
Simcenter Amesim is a commercial simulation software for the modeling and analysis of multi-domain systems. It is part of systems engineering domain and falls into the mechatronic engineering field. The software package is a suite of tools used to model, analyze and predict the performance of mechatronics systems.
A agent-based [20] modeling framework for multicellular systems biology. multiplatform (C++) BSD-3: Yes, but only for reactions PySCeS: Python tool for modeling and analyzing SBML models [21] [22] [23] multiplatform (Python) BSD-3: Yes pySB: Python-based [24] platform with specialization in rule-based models. multiplatform (Python) BSD-3 ...
GAMA was designed to allow domain experts without a programming background to model phenomena from their field of expertise. [7] The GAMA environment enables exploration of emergent phenomena. It comes with a models library including examples from several domains, such as economics, biology, physics, chemistry, psychology, and system dynamics. [8]
RMS 2010 was released in February 2010. RMS 2010 included major improvements across the entire workflow, with a wide-ranging makeover of the well correlation tools, new model building and property modelling tools and improved 3D gridding and better communication with external simulators.
Linear errors-in-variables models were studied first, probably because linear models were so widely used and they are easier than non-linear ones. Unlike standard least squares regression (OLS), extending errors in variables regression (EiV) from the simple to the multivariable case is not straightforward, unless one treats all variables in the same way i.e. assume equal reliability.