107 citations · 1k across the 45 of their papers we have counts for
6 papers · 2 filters
Dynamic Neural Program Embedding for Program Repair
Ke Wang, Rishabh Singh, Zhendong Su
Neural program embeddings have shown much promise recently for a variety of program analysis tasks, including program synthesis, program repair, fault localization, etc. However, m…
Semantic Code Repair using Neuro-Symbolic Transformation Networks
Jacob Devlin, Jonathan Uesato, Rishabh Singh +1
We study the problem of semantic code repair, which can be broadly defined as automatically fixing non-syntactic bugs in source code. The majority of past work in semantic code rep…
Neural Program Meta-Induction
Jacob Devlin, Rudy Bunel, Rishabh Singh +2
Most recently proposed methods for Neural Program Induction work under the assumption of having a large set of input/output (I/O) examples for learning any underlying input-output…
Deep API Programmer: Learning to Program with APIs
Surya Bhupatiraju, Rishabh Singh, Abdel-rahman Mohamed +1
We present DAPIP, a Programming-By-Example system that learns to program with APIs to perform data transformation tasks. We design a domain-specific language (DSL) that allows for…
RobustFill: Neural Program Learning under Noisy I/O
Jacob Devlin, Jonathan Uesato, Surya Bhupatiraju +3
The problem of automatically generating a computer program from some specification has been studied since the early days of AI. Recently, two competing approaches for automatic pro…
Learn&Fuzz: Machine Learning for Input Fuzzing
Patrice Godefroid, Hila Peleg, Rishabh Singh
Fuzzing consists of repeatedly testing an application with modified, or fuzzed, inputs with the goal of finding security vulnerabilities in input-parsing code. In this paper, we sh…