Search Results for author: Neel Sundaresan

Found 28 papers, 10 papers with code

Predicting Code Coverage without Execution

1 code implementation25 Jul 2023 Michele Tufano, Shubham Chandel, Anisha Agarwal, Neel Sundaresan, Colin Clement

Using Machine Learning to amortize this expensive process could lower the cost of code coverage by requiring only the source code context, and the task of code coverage prediction can be a novel benchmark for judging the ability of models to understand code.

RAPGen: An Approach for Fixing Code Inefficiencies in Zero-Shot

no code implementations29 Jun 2023 Spandan Garg, Roshanak Zilouchian Moghaddam, Neel Sundaresan

We compare our approach with the various prompt variations and state of the art methods in the task of performance bug fixing.

Language Modelling Large Language Model +1

Transformer-based Vulnerability Detection in Code at EditTime: Zero-shot, Few-shot, or Fine-tuning?

no code implementations23 May 2023 Aaron Chan, Anant Kharkar, Roshanak Zilouchian Moghaddam, Yevhen Mohylevskyy, Alec Helyar, Eslam Kamal, Mohamed Elkamhawy, Neel Sundaresan

We recognize that the current advances in machine learning can be used to detect vulnerable code patterns on syntactically incomplete code snippets as the developer is writing the code at EditTime.

Vulnerability Detection

Code Execution with Pre-trained Language Models

1 code implementation8 May 2023 Chenxiao Liu, Shuai Lu, Weizhu Chen, Daxin Jiang, Alexey Svyatkovskiy, Shengyu Fu, Neel Sundaresan, Nan Duan

Code execution is a fundamental aspect of programming language semantics that reflects the exact behavior of the code.

Code Generation Code Search +2

Exploring and Evaluating Personalized Models for Code Generation

no code implementations29 Aug 2022 Andrei Zlotchevski, Dawn Drain, Alexey Svyatkovskiy, Colin Clement, Neel Sundaresan, Michele Tufano

Large Transformer models achieved the state-of-the-art status for Natural Language Understanding tasks and are increasingly becoming the baseline model architecture for modeling source code.

Code Generation Natural Language Understanding +1

DeepPERF: A Deep Learning-Based Approach For Improving Software Performance

no code implementations27 Jun 2022 Spandan Garg, Roshanak Zilouchian Moghaddam, Colin B. Clement, Neel Sundaresan, Chen Wu

Additionally, we evaluate DeepPERF on 50 open source C# repositories on GitHub using both benchmark and unit tests and find that our model is able to suggest valid performance improvements that can improve both CPU usage and Memory allocations.

AdaptivePaste: Code Adaptation through Learning Semantics-aware Variable Usage Representations

no code implementations23 May 2022 Xiaoyu Liu, Jinu Jang, Neel Sundaresan, Miltiadis Allamanis, Alexey Svyatkovskiy

This scenario motivates the code adaptation task -- a variant of program repair which aims to adapt variable identifiers in a pasted snippet of code to the surrounding, preexisting source code.

Program Repair

Generating Examples From CLI Usage: Can Transformers Help?

no code implementations27 Apr 2022 Roshanak Zilouchian Moghaddam, Spandan Garg, Colin B. Clement, Yevhen Mohylevskyy, Neel Sundaresan

Continuous evolution in modern software often causes documentation, tutorials, and examples to be out of sync with changing interfaces and frameworks.

BIG-bench Machine Learning

Learning to Reduce False Positives in Analytic Bug Detectors

no code implementations8 Mar 2022 Anant Kharkar, Roshanak Zilouchian Moghaddam, Matthew Jin, Xiaoyu Liu, Xin Shi, Colin Clement, Neel Sundaresan

Due to increasingly complex software design and rapid iterative development, code defects and security vulnerabilities are prevalent in modern software.

Training and Evaluating a Jupyter Notebook Data Science Assistant

1 code implementation30 Jan 2022 Shubham Chandel, Colin B. Clement, Guillermo Serrato, Neel Sundaresan

We study the feasibility of a Data Science assistant powered by a sequence-to-sequence transformer by training a new model JuPyT5 on all publicly available Jupyter Notebook GitHub repositories and developing a new metric: Data Science Problems (DSP).

Long-Range Modeling of Source Code Files with eWASH: Extended Window Access by Syntax Hierarchy

no code implementations EMNLP 2021 Colin B. Clement, Shuai Lu, Xiaoyu Liu, Michele Tufano, Dawn Drain, Nan Duan, Neel Sundaresan, Alexey Svyatkovskiy

While there are many efforts to extend the context window, we introduce an architecture-independent approach for leveraging the syntactic hierarchies of source code for incorporating entire file-level context into a fixed-length window.

Code Completion Code Generation +3

Program Merge Conflict Resolution via Neural Transformers

1 code implementation31 Aug 2021 Alexey Svyatkovskiy, Sarah Fakhoury, Negar Ghorbani, Todd Mytkowicz, Elizabeth Dinella, Christian Bird, Jinu Jang, Neel Sundaresan, Shuvendu Lahiri

Our model achieves 63-68% accuracy for merge resolution synthesis, yielding nearly a 3x performance improvement over existing semi-structured, and 2x improvement over neural program merge tools.

Distilling Transformers for Neural Cross-Domain Search

no code implementations6 Aug 2021 Colin B. Clement, Chen Wu, Dawn Drain, Neel Sundaresan

Pre-trained transformers have recently clinched top spots in the gamut of natural language tasks and pioneered solutions to software engineering tasks.

Code Search Data Augmentation +3

DeepDebug: Fixing Python Bugs Using Stack Traces, Backtranslation, and Code Skeletons

no code implementations19 May 2021 Dawn Drain, Colin B. Clement, Guillermo Serrato, Neel Sundaresan

The joint task of bug localization and program repair is an integral part of the software development process.

Program Repair

Generating Bug-Fixes Using Pretrained Transformers

no code implementations16 Apr 2021 Dawn Drain, Chen Wu, Alexey Svyatkovskiy, Neel Sundaresan

In this work we introduce DeepDebug: a data-driven program repair approach which learns to detect and fix bugs in Java methods mined from real-world GitHub repositories.

Denoising Program Repair

Generating Code with the Help of Retrieved Template Functions and Stack Overflow Answers

no code implementations12 Apr 2021 Dawn Drain, Changran Hu, Chen Wu, Mikhail Breslav, Neel Sundaresan

To demonstrate the effectiveness of our model designs, we perform extensive experiments with CodeSearchNet which contains template functions and CoNaLa which contains Stack Overflow intent-snippet pairs.

Code Search Retrieval

PyMT5: multi-mode translation of natural language and Python code with transformers

no code implementations EMNLP 2020 Colin B. Clement, Dawn Drain, Jonathan Timcheck, Alexey Svyatkovskiy, Neel Sundaresan

Simultaneously modeling source code and natural language has many exciting applications in automated software development and understanding.


CodeBLEU: a Method for Automatic Evaluation of Code Synthesis

1 code implementation22 Sep 2020 Shuo Ren, Daya Guo, Shuai Lu, Long Zhou, Shujie Liu, Duyu Tang, Neel Sundaresan, Ming Zhou, Ambrosio Blanco, Shuai Ma

Evaluation metrics play a vital role in the growth of an area as it defines the standard of distinguishing between good and bad models.

Code Translation Translation

GraphCodeBERT: Pre-training Code Representations with Data Flow

1 code implementation ICLR 2021 Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, Shujie Liu, Long Zhou, Nan Duan, Alexey Svyatkovskiy, Shengyu Fu, Michele Tufano, Shao Kun Deng, Colin Clement, Dawn Drain, Neel Sundaresan, Jian Yin, Daxin Jiang, Ming Zhou

Instead of taking syntactic-level structure of code like abstract syntax tree (AST), we use data flow in the pre-training stage, which is a semantic-level structure of code that encodes the relation of "where-the-value-comes-from" between variables.

Clone Detection Code Completion +7

Generating Accurate Assert Statements for Unit Test Cases using Pretrained Transformers

no code implementations11 Sep 2020 Michele Tufano, Dawn Drain, Alexey Svyatkovskiy, Neel Sundaresan

In this paper we present an approach to support developers in writing unit test cases by generating accurate and useful assert statements.

Unit Test Case Generation with Transformers and Focal Context

1 code implementation11 Sep 2020 Michele Tufano, Dawn Drain, Alexey Svyatkovskiy, Shao Kun Deng, Neel Sundaresan

We execute the test cases, collect test coverage information, and compare them with test cases generated by EvoSuite and GPT-3, finding that our approach outperforms GPT-3 and has comparable coverage w. r. t.


IntelliCode Compose: Code Generation Using Transformer

no code implementations16 May 2020 Alexey Svyatkovskiy, Shao Kun Deng, Shengyu Fu, Neel Sundaresan

In software development through integrated development environments (IDEs), code completion is one of the most widely used features.

Code Completion Code Generation

Pythia: AI-assisted Code Completion System

1 code implementation29 Nov 2019 Alexey Svyatkovskiy, Ying Zhao, Shengyu Fu, Neel Sundaresan

In this paper, we propose a novel end-to-end approach for AI-assisted code completion called Pythia.

Code Completion Language Modelling

Fast Approximate Matching of Cell-Phone Videos for Robust Background Subtraction

no code implementations22 Apr 2014 Raffay Hamid, Atish Das Sarma, Dennis Decoste, Neel Sundaresan

We identify a novel instance of the background subtraction problem that focuses on extracting near-field foreground objects captured using handheld cameras.

Large-Scale Video Summarization Using Web-Image Priors

no code implementations CVPR 2013 Aditya Khosla, Raffay Hamid, Chih-Jen Lin, Neel Sundaresan

Given the enormous growth in user-generated videos, it is becoming increasingly important to be able to navigate them efficiently.

Navigate Video Summarization

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