MODIST's Key Features - a summary

MODIST provides an integrated approach to managing Uncertainty, Risk and Quality.

MODIST's causal models capture the impact of human factors on software development far more effectively than statistical models.

MODIST scales up the application of modelling based on Bayesian Networks to accommodate complex software development scenarios. End users benefit from the Bayesian Network technology without having to understand the complexity of the underlying models.

MODIST's efficient algorithms provide rapid feedback to the user in response to the entry of new evidence.

MODIST's scope means that users can investigate trade-offs among key factors relating to quality, functionality, effort and schedule, and so make decisions that are better informed.

MODIST's modular network templates make it scalable to accommodate projects of different sizes, or to analyse issues at varying levels of detail.

MODIST's layered architecture and tools for tailoring make it adaptable to widely varying project characteristics.

MODIST makes the quantitative assessment of risk and quality feasible without dependence on an extensive metrics programme, in that expert judgment can be used to supplement whatever statistical data is available.