20 citations · 22 across the 3 of their papers we have counts for
13 papers
MP-CodeCheck: Evolving Logical Expression Code Anomaly Learning with Iterative Self-Supervision
Urs C. Muff, Celine Lee, Paul Gottschlich +1
Machine programming (MP) is concerned with automating software development. According to studies, software engineers spend upwards of 50% of their development time debugging softwa…
Toward Code Generation: A Survey and Lessons from Semantic Parsing
Celine Lee, Justin Gottschlich, Dan Roth
With the growth of natural language processing techniques and demand for improved software engineering efficiency, there is an emerging interest in translating intention from human…
ControlFlag: A Self-Supervised Idiosyncratic Pattern Detection System for Software Control Structures
Niranjan Hasabnis, Justin Gottschlich
Software debugging has been shown to utilize upwards of half of developers' time. Yet, machine programming (MP), the field concerned with the automation of software (and hardware)…
Software Language Comprehension using a Program-Derived Semantics Graph
Roshni G. Iyer, Yizhou Sun, Wei Wang +1
Traditional code transformation structures, such as abstract syntax trees (ASTs), conteXtual flow graphs (XFGs), and more generally, compiler intermediate representations (IRs), ma…
Context-Aware Parse Trees
Fangke Ye, Shengtian Zhou, Anand Venkat +8
The simplified parse tree (SPT) presented in Aroma, a state-of-the-art code recommendation system, is a tree-structured representation used to infer code semantics by capturing pro…
An Abstraction-Based Framework for Neural Network Verification
Yizhak Yisrael Elboher, Justin Gottschlich, Guy Katz
Deep neural networks are increasingly being used as controllers for safety-critical systems. Because neural networks are opaque, certifying their correctness is a significant chall…