Top-Down Synthesis for Library Learning

This thesis introduces corpus-guided top-down synthesis as a mechanism for synthesizing library functions that capture common functionality from a corpus of programs in a domain specific language (DSL). The algorithm builds abstractions directly from initial DSL primitives, using syntactic pattern matching of intermediate abstractions to intelligently prune the search space and guide the algorithm towards abstractions that maximally capture shared structures in the corpus. We present an implementation of the approach in a tool called Stitch and evaluate it against the state-of-the-art deductive library learning algorithm from DreamCoder. Our evaluation shows that Stitch is 3-4 orders of magnitude faster and uses 2 orders of magnitude less memory while maintaining comparable or better library quality (as measured by compressivity). We also demonstrate Stitch’s scalability on corpora containing hundreds of complex programs that are intractable with prior deductive approaches and show empirically that it is robust to terminating the search procedure early—further allowing it to scale to challenging datasets by means of early stopping. We publish the code, the documentation, a tutorial, and a Python library for interfacing with our for our Rust implementation of Stitch. Tutorial & Documentation (Python Library): https://stitch-bindings.read thedocs.io/en/stable/intro/tutorial.html Rust Implementation: https://github.com/mlb2251/stitch Artifact (Awarded: Reusable): https://github.com/mlb2251/stitch-artifact

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2023

Author dc:creator

• Bowers, Matthew L.

Advisor dc:contributor.advisor

• Solar-Lezama, Armando

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/151374

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/151374