212 citations · 810 across the 54 of their papers we have counts for
57 papers
TRACED: Execution-aware Pre-training for Source Code
Yangruibo Ding, Ben Steenhoek, Kexin Pei +3
Most existing pre-trained language models for source code focus on learning the static code text, typically augmented with static code structures (abstract syntax tree, dependency…
CONCORD: Clone-aware Contrastive Learning for Source Code
Yangruibo Ding, Saikat Chakraborty, Luca Buratti +4
Deep Learning (DL) models to analyze source code have shown immense promise during the past few years. More recently, self-supervised pre-training has gained traction for learning…
A Static Evaluation of Code Completion by Large Language Models
Hantian Ding, Varun Kumar, Yuchen Tian +9
Large language models trained on code have shown great potential to increase productivity of software developers. Several execution-based benchmarks have been proposed to evaluate…
CAMEO: A Causal Transfer Learning Approach for Performance Optimization of Configurable Computer Systems
Md Shahriar Iqbal, Ziyuan Zhong, Iftakhar Ahmad +2
Modern computer systems are highly configurable, with hundreds of configuration options that interact, resulting in an enormous configuration space. As a result, optimizing perform…
Automated Code Editing with Search-Generate-Modify
Changshu Liu, Pelin Cetin, Yogesh Patodia +3
Code editing is essential in evolving software development. Many automated code editing tools have been proposed that leverage both Information Retrieval-based techniques and Machi…
Language-Guided Traffic Simulation via Scene-Level Diffusion
Ziyuan Zhong, Davis Rempe, Yuxiao Chen +5
Realistic and controllable traffic simulation is a core capability that is necessary to accelerate autonomous vehicle (AV) development. However, current approaches for controlling…
TraceFixer: Execution Trace-Driven Program Repair
Islem Bouzenia, Yangruibo Ding, Kexin Pei +2
When debugging unintended program behavior, developers can often identify the point in the execution where the actual behavior diverges from the desired behavior. For example, a va…
Variation of Gender Biases in Visual Recognition Models Before and After Finetuning
Jaspreet Ranjit, Tianlu Wang, Baishakhi Ray +1
We introduce a framework to measure how biases change before and after fine-tuning a large scale visual recognition model for a downstream task. Deep learning models trained on inc…
Greener yet Powerful: Taming Large Code Generation Models with Quantization
Xiaokai Wei, Sujan Gonugondla, Wasi Ahmad +13
ML-powered code generation aims to assist developers to write code in a more productive manner, by intelligently generating code blocks based on natural language prompts. Recently,…
On ML-Based Program Translation: Perils and Promises
Aniketh Malyala, Katelyn Zhou, Baishakhi Ray +1
With the advent of new and advanced programming languages, it becomes imperative to migrate legacy software to new programming languages. Unsupervised Machine Learning-based Progra…
ReCode: Robustness Evaluation of Code Generation Models
Shiqi Wang, Zheng Li, Haifeng Qian +11
Code generation models have achieved impressive performance. However, they tend to be brittle as slight edits to a prompt could lead to very different generations; these robustness…
Guided Conditional Diffusion for Controllable Traffic Simulation
Ziyuan Zhong, Davis Rempe, Danfei Xu +5
Controllable and realistic traffic simulation is critical for developing and verifying autonomous vehicles. Typical heuristic-based traffic models offer flexible control to make ve…
NeuDep: Neural Binary Memory Dependence Analysis
Kexin Pei, Dongdong She, Michael Wang +6
Determining whether multiple instructions can access the same memory location is a critical task in binary analysis. It is challenging as statically computing precise alias informa…
Multi-lingual Evaluation of Code Generation Models
Ben Athiwaratkun, Sanjay Krishna Gouda, Zijian Wang +22
We present new benchmarks on evaluation code generation models: MBXP and Multilingual HumanEval, and MathQA-X. These datasets cover over 10 programming languages and are generated…
ContraCLM: Contrastive Learning For Causal Language Model
Nihal Jain, Dejiao Zhang, Wasi Uddin Ahmad +9
Despite exciting progress in causal language models, the expressiveness of the representations is largely limited due to poor discrimination ability. To remedy this issue, we prese…
IvySyn: Automated Vulnerability Discovery in Deep Learning Frameworks
Neophytos Christou, Di Jin, Vaggelis Atlidakis +2
We present IvySyn, the first fully-automated framework for discovering memory error vulnerabilities in Deep Learning (DL) frameworks. IvySyn leverages the statically-typed nature o…
NatGen: Generative pre-training by "Naturalizing" source code
Saikat Chakraborty, Toufique Ahmed, Yangruibo Ding +2
Pre-trained Generative Language models (e.g. PLBART, CodeT5, SPT-Code) for source code yielded strong results on several tasks in the past few years, including code generation and…
CARGO: AI-Guided Dependency Analysis for Migrating Monolithic Applications to Microservices Architecture
Vikram Nitin, Shubhi Asthana, Baishakhi Ray +1
Microservices Architecture (MSA) has become a de-facto standard for designing cloud-native enterprise applications due to its efficient infrastructure setup, service availability,…
Automatic Map Generation for Autonomous Driving System Testing
Yun Tang, Yuan Zhou, Kairui Yang +5
High-definition (HD) maps are essential in testing autonomous driving systems (ADSs). HD maps essentially determine the potential diversity of the testing scenarios. However, the c…
Summarize and Generate to Back-translate: Unsupervised Translation of Programming Languages
Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray +1
Back-translation is widely known for its effectiveness in neural machine translation when there is little to no parallel data. In this approach, a source-to-target model is coupled…
Repairing Group-Level Errors for DNNs Using Weighted Regularization
Ziyuan Zhong, Yuchi Tian, Conor J. Sweeney +2
Deep Neural Networks (DNNs) have been widely used in software making decisions impacting people's lives. However, they have been found to exhibit severe erroneous behaviors that ma…
VELVET: a noVel Ensemble Learning approach to automatically locate VulnErable sTatements
Yangruibo Ding, Sahil Suneja, Yunhui Zheng +4
Automatically locating vulnerable statements in source code is crucial to assure software security and alleviate developers' debugging efforts. This becomes even more important in…
Unicorn: Reasoning about Configurable System Performance through the lens of Causality
Md Shahriar Iqbal, Rahul Krishna, Mohammad Ali Javidian +2
Modern computer systems are highly configurable, with the total variability space sometimes larger than the number of atoms in the universe. Understanding and reasoning about the p…
Predictive Synthesis of API-Centric Code
Daye Nam, Baishakhi Ray, Seohyun Kim +2
Today's programmers, especially data science practitioners, make heavy use of data-processing libraries (APIs) such as PyTorch, Tensorflow, NumPy, Pandas, and the like. Program syn…