  Recent research in synthesis of programs written in some Domain Specific
Language (DSL) by means of neural networks from a limited set of inputs-output
correspondences such as DeepCoder and its PCCoder reimplementation/optimization
proved the efficiency of this kind of approach to automatic program generation
in a DSL language that although limited in scope is universal in the sense that
programs can be translated to basically any programming language. We experiment
with the extension of the DSL of DeepCoder/PCCoder with symbols IFI and IFL
which denote functional expressions of the If ramification (test) instruction
for types Int and List. We notice an increase (doubling) of the size of the
training set, the number of parameters of the trained neural network and of the
time spent looking for the program synthesized from limited sets of
inputs-output correspondences. The result is positive in the sense of
preserving the accuracy of applying synthesis on randomly generated test sets.
