Tools solving the program synthesis problem often take as input partial specification, whether in the form of logical formulas, types or examples. Human users tend to specify just enough to explain the needed solution to themselves, and thus expose the gap between human thought, which is capable of generalization from the partial specificationf, and synthesis tools – and their backend ML models – which are only able to generalize once the form of bias has been selected for them.

Humans are often able to generalize better than algorithms. The language of classifiers of a ML algorithm, which creates its built-in bias, it is still no match for human common sense, which allows for selection from a much larger range of generalization mechanisms. Synthesis tools often address this issue by ranking the possible biases of the tool’s inner learning based on domain-specific information, but this does not scale when more generalizations are added to the tool, nor does it generalize when a synthesis algorithm is transferred to a different problem domain.

We wish to explore methods in which the human user can apply common sense: to select one of several possible biases, to refine specifications so that existing biases are no longer relevant, or to simply intervene manually in the result. In addition, we ask what the limitation of this human intervention is, since common sense can depend on many factors, first and foremost an understanding of the problem being solved and the solution being proposed by the synthesizer.