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Programming or scripting languages used in real-world systems are seldomdesigned with a formal semantics in mind from the outset. Therefore, developingwell-founded analysis tools for these systems requires reverse-engineering aformal semantics as a first step. This can take months or years of effort.  Can we (at least partially) automate this process? Though desirable,automatically reverse-engineering semantics rules from an implementation isvery challenging, as found by Krishnamurthi et al. [2019]. In this paper, wehighlight that scaling methods with the size of the language is very difficultdue to state space explosion, so we propose to learn semantics incrementally.We give a formalisation of Krishnamurthi et al.'s desugaring learning frameworkin order to clarify the assumptions necessary for an incremental learningalgorithm to be feasible.  We show that this reformulation allows us to extend the search space andexpress rules that Krishnamurthi et al. described as challenging, while stillretaining feasibility. We evaluate enumerative synthesis as a baselinealgorithm, and demonstrate that, with our reformulation of the problem, it ispossible to learn correct desugaring rules for the example source and corelanguages proposed by Krishnamurthi et al., in most cases identical to theintended rules. In addition, with user guidance, our system was able tosynthesize rules for desugaring list comprehensions and try/catch/finallyconstructs.

One Down, 699 to Go: or, synthesising compositional desugarings