
TL;DR
This paper extends the PCCoder system with new functional symbols, increasing training complexity but maintaining synthesis accuracy, demonstrating the potential for more expressive program synthesis in a neural network framework.
Contribution
The paper introduces new functional symbols into the PCCoder DSL, expanding its expressiveness and analyzing the impact on training and synthesis performance.
Findings
Training set size doubled with new symbols
Neural network parameters increased significantly
Synthesis accuracy remained stable on test sets
Abstract
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…
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Taxonomy
TopicsSoftware Testing and Debugging Techniques · Software Engineering Research · Parallel Computing and Optimization Techniques
MethodsTest
