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Another issue that has to be addressed in the fu-
ture is the complexity of transition conditions. We
have concentrated on states and transition structure so
far. However, transition conditions are also important
for understanding. The conditions that result from the
extraction process are usually extremely complex and
not understandable for humans. Also, reducing the
number of states obviously makes state machine mod-
els more understandable. However, the information
that was previously contained in the state invariants
will not get lost, but it will move into the transitions,
which may make those more complex. We are cur-
rently investigating approaches for reducing this com-
plexity. For example, not all information in these con-
ditions is relevant. This could lead to another interac-
tive extension.
Other opportunities for future research include ad-
ditional interactions, automated identification of ef-
fective constraints, and runtime optimization for the
SMT solver. We also want to apply similar interac-
tions to extraction of other kinds of models that can
support developers in reengineering tasks.
In summary, adding interaction to state machine
mining appears to be a promising approach towards
practically usable support for program comprehen-
sion and migration towards model-based develop-
ment.
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