92 citations · 252 across the 14 of their papers we have counts for
6 papers · 1 filter
Testing Neural Program Analyzers
Md Rafiqul Islam Rabin, Ke Wang, Mohammad Amin Alipour
Deep neural networks have been increasingly used in software engineering and program analysis tasks. They usually take a program and make some predictions about it, e.g., bug predi…
Learning Blended, Precise Semantic Program Embeddings
Ke Wang, Zhendong Su
Learning neural program embeddings is key to utilizing deep neural networks in program languages research --- precise and efficient program representations enable the application o…
Learning a Static Bug Finder from Data
Yu Wang, Fengjuan Gao, Linzhang Wang +1
We present an alternative approach to creating static bug finders. Instead of relying on human expertise, we utilize deep neural networks to train static analyzers directly from da…
COSET: A Benchmark for Evaluating Neural Program Embeddings
Ke Wang, Mihai Christodorescu
Neural program embedding can be helpful in analyzing large software, a task that is challenging for traditional logic-based program analyses due to their limited scalability. A key…
Learning Scalable and Precise Representation of Program Semantics
Ke Wang
Neural program embedding has shown potential in aiding the analysis of large-scale, complicated software. Newly proposed deep neural architectures pride themselves on learning prog…
Reduce Transmission Delay for Cache-Aided Two-Layer Network
Ke Wang, Youlong Wu, Shujie Cao +1
In this paper, we consider a cache-aided relay network, where a single server consisting of a library of N files connects with K1 relays through a shared noiseless link, and each r…