377 citations · 818 across the 21 of their papers we have counts for
23 papers
ItyFuzz: Snapshot-Based Fuzzer for Smart Contract
Chaofan Shou, Shangyin Tan, Koushik Sen
Smart contracts are critical financial instruments, and their security is of utmost importance. However, smart contract programs are difficult to fuzz due to the persistent blockch…
SlimFit: Memory-Efficient Fine-Tuning of Transformer-based Models Using Training Dynamics
Arash Ardakani, Altan Haan, Shangyin Tan +4
Transformer-based models, such as BERT and ViT, have achieved state-of-the-art results across different natural language processing (NLP) and computer vision (CV) tasks. However, t…
Benchmarking Language Models for Code Syntax Understanding
Da Shen, Xinyun Chen, Chenguang Wang +2
Pre-trained language models have demonstrated impressive performance in both natural language processing and program understanding, which represent the input as a token sequence wi…
PALT: Parameter-Lite Transfer of Language Models for Knowledge Graph Completion
Jianhao Shen, Chenguang Wang, Ye Yuan +5
This paper presents a parameter-lite transfer learning approach of pretrained language models (LM) for knowledge graph (KG) completion. Instead of finetuning, which modifies all LM…
LGV: Boosting Adversarial Example Transferability from Large Geometric Vicinity
Martin Gubri, Maxime Cordy, Mike Papadakis +2
We propose transferability from Large Geometric Vicinity (LGV), a new technique to increase the transferability of black-box adversarial attacks. LGV starts from a pretrained surro…
The Sky Above The Clouds
Sarah Chasins, Alvin Cheung, Natacha Crooks +14
Technology ecosystems often undergo significant transformations as they mature. For example, telephony, the Internet, and PCs all started with a single provider, but in the United…
Learning Highly Recursive Input Grammars
Neil Kulkarni, Caroline Lemieux, Koushik Sen
This paper presents Arvada, an algorithm for learning context-free grammars from a set of positive examples and a Boolean-valued oracle. Arvada learns a context-free grammar by bui…
QFAST: Conflating Search and Numerical Optimization for Scalable Quantum Circuit Synthesis
Ed Younis, Koushik Sen, Katherine Yelick +1
We present a quantum synthesis algorithm designed to produce short circuits and to scale well in practice. The main contribution is a novel representation of circuits able to encod…
Growing a Test Corpus with Bonsai Fuzzing
Vasudev Vikram, Rohan Padhye, Koushik Sen
This paper presents a coverage-guided grammar-based fuzzing technique for automatically generating a corpus of concise test inputs for programs such as compilers. We walk-through a…
Efficient and Transferable Adversarial Examples from Bayesian Neural Networks
Martin Gubri, Maxime Cordy, Mike Papadakis +2
An established way to improve the transferability of black-box evasion attacks is to craft the adversarial examples on an ensemble-based surrogate to increase diversity. We argue t…
Hindsight Logging for Model Training
Rolando Garcia, Eric Liu, Vikram Sreekanti +5
In modern Machine Learning, model training is an iterative, experimental process that can consume enormous computation resources and developer time. To aid in that process, experie…
Ansor: Generating High-Performance Tensor Programs for Deep Learning
Lianmin Zheng, Chengfan Jia, Minmin Sun +9
High-performance tensor programs are crucial to guarantee efficient execution of deep neural networks. However, obtaining performant tensor programs for different operators on vari…
QFAST: Quantum Synthesis Using a Hierarchical Continuous Circuit Space
Ed Younis, Koushik Sen, Katherine Yelick +1
We present QFAST, a quantum synthesis tool designed to produce short circuits and to scale well in practice. Our contributions are: 1) a novel representation of circuits able to en…
Heuristics for Quantum Compiling with a Continuous Gate Set
Marc Grau Davis, Ethan Smith, Ana Tudor +3
We present an algorithm for compiling arbitrary unitaries into a sequence of gates native to a quantum processor. As accurate CNOT gates are hard for the foreseeable Noisy- Interme…
When Deep Learning Met Code Search
Jose Cambronero, Hongyu Li, Seohyun Kim +2
There have been multiple recent proposals on using deep neural networks for code search using natural language. Common across these proposals is the idea of co…
Aroma: Code Recommendation via Structural Code Search
Sifei Luan, Di Yang, Celeste Barnaby +2
Programmers often write code that has similarity to existing code written somewhere. A tool that could help programmers to search such similar code would be immensely useful. Such…
Semantic Fuzzing with Zest
Rohan Padhye, Caroline Lemieux, Koushik Sen +2
Programs expecting structured inputs often consist of both a syntactic analysis stage, which parses raw input, and a semantic analysis stage, which conducts checks on the parsed in…
Context2Name: A Deep Learning-Based Approach to Infer Natural Variable Names from Usage Contexts
Rohan Bavishi, Michael Pradel, Koushik Sen
Most of the JavaScript code deployed in the wild has been minified, a process in which identifier names are replaced with short, arbitrary and meaningless names. Minified code occu…
DeepBugs: A Learning Approach to Name-based Bug Detection
Michael Pradel, Koushik Sen
Natural language elements in source code, e.g., the names of variables and functions, convey useful information. However, most existing bug detection tools ignore this information…
Selecting Fault Revealing Mutants
Thierry Titcheu Chekam, Mike Papadakis, Tegawendé Bissyandé +2
Mutant selection refers to the problem of choosing, among a large number of mutants, the (few) ones that should be used by the testers. In view of this, we investigate the problem…
FairFuzz: Targeting Rare Branches to Rapidly Increase Greybox Fuzz Testing Coverage
Caroline Lemieux, Koushik Sen
In recent years, fuzz testing has proven itself to be one of the most effective techniques for finding correctness bugs and security vulnerabilities in practice. One particular fuz…
Report of the HPC Correctness Summit, Jan 25--26, 2017, Washington, DC
Ganesh Gopalakrishnan, Paul D. Hovland, Costin Iancu +6
Maintaining leadership in HPC requires the ability to support simulations at large scales and fidelity. In this study, we detail one of the most significant productivity challenges…
Trace Typing: An Approach for Evaluating Retrofitted Type Systems (Extended Version)
Esben Andreasen, Colin S. Gordon, Satish Chandra +3
Recent years have seen growing interest in the retrofitting of type systems onto dynamically-typed programming languages, in order to improve type safety, programmer productivity,…