The DFM 2.2 specification will be released on 1 Oct 2010. It introduces:

- Time-dependent variables
- States can be assigned time-dependent probabilities if:
- The node is binary
- The node is multi-state and the states are disjoint

- The time duration can be:
- Constant (specified by the user)
- Measured from the start of the analysis
- Measured from the occurrence of an intermediate DFM Event

- Time-dependent probabilities can be calculated from:
- Rates
- Probability distributions

- States can be assigned time-dependent probabilities if:

The DFM 2.1 specification introduced:

- Variable-delay Transition Boxes
- Previously, the delay was a characteristic of the Transition Box and every Edge into a particular Transition Box necessarily had the same delay.
- Modeling transfer functions that used state information from multiple time-steps required the use of history Nodes that stored state information from previous time-steps.
- A Transition Box was defined as a Transfer Box with a non-zero delay.

- From DFM 2.1, the delay is a characteristic of the Edge that enters the Transition Box.
- Each Edge into a particular Transfer Box can have a distinct delay.
- Feedback loops across multiple time-steps can now be modeled directly and history Nodes are now obsolete.
- A Transfer Box can have multiple input Edges from the same Node, as long as each Edge has a different delay
- A Transition Box is now defined as a Transfer Box with at least one input Edge with a non-zero delay.

- Previously, the delay was a characteristic of the Transition Box and every Edge into a particular Transition Box necessarily had the same delay.

DFM 2.0 introduced two new features: probabilities and quantification.

- Probabilities
- The states of a source node can be assigned point probabilities.
- If a model contains states with probabilities, it's DFM Events, Prime Implicants, and DFM Results have probabilities determined by the probabilities of the states they contain.

- Quantification
- Upper Bound
- The quantification of a Complete Base produces an upper bound for the probability of the Top Event occurring.
- If the Prime Implicants are mutually exclusive, the upper bound is also the real probability.
- If Prime Implicants share events, the shared events are over-counted and the result is an upper bound of the true result.

- The upper bound is the equivalent of the Min Cut Upper Bound used by Fault Tree/Event Tree methods.

- The quantification of a Complete Base produces an upper bound for the probability of the Top Event occurring.
- Exact Quantification
- The Complete Base can be converted into a set of Mutually Exclusive Implicants.
- Because they are mutually exclusive, quantifying the set of Mutually Exclusive Implicants always produces the exact result.

- Upper Bound

DFM 1.0 was the first release of DFM.

It included all of the current DFM structures:

- Directed graph representation
- Nodes
- Discrete nodes
- Continuous nodes
- All nodes are discretized into multiple states

- Transfer boxes
- Transfer boxes with no time delay
- Transition boxes with time delays
- Boxes contain a decision table that maps discrete input combinations to discrete output combinations

- Directed edges
- Causal Edges
- Conditioning Edges

It also contained the basic DFM analysis capabilities:

- Inductive Analysis
- Directed-flow
*(simulation mode)*- Explores a single path through the DFM model

- Exhaustive
*(analysis mode)*- Generates all possible events that can result from the Initiating Event

- Directed-flow
- Deductive Analysis
- Generates all possible initial states that could have produced the Top Event