Abstract
Program synthesis aims to produce source code based on a user specification, raising the abstraction level of building systems and opening the potential for non-programmers to synthesise their own bespoke services. Both genetic programming (GP) and neural code synthesis have proposed a wide range of approaches to solving this problem, but both have limitations in generality and scope. We propose a hybrid search-based approach which combines (i) a genetic algorithm to autonomously generate a training corpus of programs centred around a set of highly abstracted hints describing interesting features; and (ii) a neural network which trains on this data and automatically refactors it towards a form which makes a more ideal use of the neural network’s representational capacity. When given an unseen program represented as a small set of input and output examples, our neural network is used to generate a rank-ordered search space of what it sees as the most promising programs; we then iterate through this list up to a given maximum search depth. Our results show that this approach is able to find up to 60% of a human-useful target set of programs that it has never seen before, including applying a clip function to the values in an array to restrict them to a given maximum, and offsetting all values in an array.
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Acknowledgements
This work was supported by the Leverhulme Trust Research Project Grant The Emergent Data Centre, RPG-2017-166.
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Wild, A., Porter, B. (2019). General Program Synthesis Using Guided Corpus Generation and Automatic Refactoring. In: Nejati, S., Gay, G. (eds) Search-Based Software Engineering. SSBSE 2019. Lecture Notes in Computer Science(), vol 11664. Springer, Cham. https://doi.org/10.1007/978-3-030-27455-9_7
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DOI: https://doi.org/10.1007/978-3-030-27455-9_7
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