107 citations · 1k across the 45 of their papers we have counts for
9 papers · 1 filter
Learning and analyzing vector encoding of symbolic representations
Roland Fernandez, Asli Celikyilmaz, Rishabh Singh +1
We present a formal language with expressions denoting general symbol structures and queries which access information in those structures. A sequence-to-sequence network processing…
Deep Reinforcement Fuzzing
Konstantin Böttinger, Patrice Godefroid, Rishabh Singh
Fuzzing is the process of finding security vulnerabilities in input-processing code by repeatedly testing the code with modified inputs. In this paper, we formalize fuzzing as a re…
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…