107 citations · 1k across the 45 of their papers we have counts for
11 papers · 1 filter
BUSTLE: Bottom-Up Program Synthesis Through Learning-Guided Exploration
Augustus Odena, Kensen Shi, David Bieber +3
Program synthesis is challenging largely because of the difficulty of search in a large space of programs. Human programmers routinely tackle the task of writing complex programs b…
TF-Coder: Program Synthesis for Tensor Manipulations
Kensen Shi, David Bieber, Rishabh Singh
The success and popularity of deep learning is on the rise, partially due to powerful deep learning frameworks such as TensorFlow and PyTorch that make it easier to develop deep le…
SyGuS-Comp 2018: Results and Analysis
Rajeev Alur, Dana Fisman, Saswat Padhi +2
Syntax-guided synthesis (SyGuS) is the computational problem of finding an implementation that meets both a semantic constraint given by a logical formula in a background t…
Program Repair via Direct State Manipulation
Qinheping Hu, Isaac Evavold, Roopsha Samanta +2
The goal of program repair is to automatically fix programs to meet a specification. We propose a new specification mechanism, direct manipulation, in which the programmer can visu…
Data-Driven Feedback Generation for Introductory Programming Exercises
Ke Wang, RIshabh Singh, Zhendong Su
This paper introduces the "Search, Align, and Repair" data-driven program repair framework to automate feedback generation for introductory programming exercises. Distinct from exi…
Program Synthesis using Abstraction Refinement
Xinyu Wang, Isil Dillig, Rishabh Singh
We present a new approach to example-guided program synthesis based on counterexample-guided abstraction refinement. Our method uses the abstract semantics of the underlying DSL to…