activity
20182022
most citedbabble: Learning Better Abstractions with E-Graphs and Anti-Unification

39 citations · 53 across the 3 of their papers we have counts for

collaborators

8 papers

cs.PL202239 cited

babble: Learning Better Abstractions with E-Graphs and Anti-Unification

David Cao, Rose Kunkel, Chandrakana Nandi +3

Library learning compresses a given corpus of programs by extracting common structure from the corpus into reusable library functions. Prior work on library learning suffers from t…

cs.PL202214 cited

Type-Directed Program Synthesis for RESTful APIs

Zheng Guo, David Cao, Davin Tjong +3

With the rise of software-as-a-service and microservice architectures, RESTful APIs are now ubiquitous in mobile and web applications. A service can have tens or hundreds of API me…

cs.PL2020

Just-in-Time Learning for Bottom-Up Enumerative Synthesis

Shraddha Barke, Hila Peleg, Nadia Polikarpova

A key challenge in program synthesis is the astronomical size of the search space the synthesizer has to explore. In response to this challenge, recent work proposed to guide synth…

cs.PL2020

Liquid Resource Types

Tristan Knoth, Di Wang, Adam Reynolds +2

This article presents liquid resource types, a technique for automatically verifying the resource consumption of functional programs. Existing resource analysis techniques trade au…

cs.PL2020

Concise Read-Only Specifications for Better Synthesis of Programs with Pointers -- Extended Version

Andreea Costea, Amy Zhu, Nadia Polikarpova +1

In program synthesis there is a well-known trade-off between concise and strong specifications: if a specification is too verbose, it might be harder to write than the program; if…

cs.PL2019

Resource-Guided Program Synthesis

Tristan Knoth, Di Wang, Nadia Polikarpova +1

This article presents resource-guided synthesis, a technique for synthesizing recursive programs that satisfy both a functional specification and a symbolic resource bound. The tec…