Search Results for author: Baishakhi Ray

Found 55 papers, 33 papers with code

DIRECT : A Transformer-based Model for Decompiled Identifier Renaming

no code implementations ACL (NLP4Prog) 2021 Vikram Nitin, Anthony Saieva, Baishakhi Ray, Gail Kaiser

Decompiling binary executables to high-level code is an important step in reverse engineering scenarios, such as malware analysis and legacy code maintenance.

Malware Analysis

Solving Zebra Puzzles Using Constraint-Guided Multi-Agent Systems

no code implementations4 Jul 2024 Shmuel Berman, Kathleen McKeown, Baishakhi Ray

We introduce a multi-agent system, ZPS, that integrates LLMs with an off the shelf theorem prover.

Reasoning in Token Economies: Budget-Aware Evaluation of LLM Reasoning Strategies

no code implementations10 Jun 2024 Junlin Wang, Siddhartha Jain, Dejiao Zhang, Baishakhi Ray, Varun Kumar, Ben Athiwaratkun

A diverse array of reasoning strategies has been proposed to elicit the capabilities of large language models.

Ingenuity

SemCoder: Training Code Language Models with Comprehensive Semantics

no code implementations3 Jun 2024 Yangruibo Ding, Jinjun Peng, Marcus J. Min, Gail Kaiser, Junfeng Yang, Baishakhi Ray

We introduce a novel strategy to train Code LLMs with comprehensive semantics, encompassing high-level functional descriptions, local execution effects of individual statements, and overall input/output behavior, thereby linking static code text with dynamic execution states.

Code Completion Code Generation +1

Training LLMs to Better Self-Debug and Explain Code

no code implementations28 May 2024 Nan Jiang, Xiaopeng Li, Shiqi Wang, Qiang Zhou, Soneya Binta Hossain, Baishakhi Ray, Varun Kumar, Xiaofei Ma, Anoop Deoras

We thus propose an automated pipeline to collect a high-quality dataset for code explanation and refinement by generating a number of explanations and refinement trajectories and filtering via execution verification.

Code Generation Reinforcement Learning (RL)

Automatic Programming: Large Language Models and Beyond

no code implementations3 May 2024 Michael R. Lyu, Baishakhi Ray, Abhik Roychoudhury, Shin Hwei Tan, Patanamon Thongtanunam

In this article, we study automated coding in a general sense and study the concerns around code quality, security and related issues of programmer responsibility.

Program Repair

CodeFort: Robust Training for Code Generation Models

no code implementations11 Apr 2024 Yuhao Zhang, Shiqi Wang, Haifeng Qian, Zijian Wang, Mingyue Shang, Linbo Liu, Sanjay Krishna Gouda, Baishakhi Ray, Murali Krishna Ramanathan, Xiaofei Ma, Anoop Deoras

Code generation models are not robust to small perturbations, which often lead to inconsistent and incorrect generations and significantly degrade the performance of these models.

Code Generation Contrastive Learning +1

CYCLE: Learning to Self-Refine the Code Generation

1 code implementation27 Mar 2024 Yangruibo Ding, Marcus J. Min, Gail Kaiser, Baishakhi Ray

Pre-trained code language models have achieved promising performance in code generation and improved the programming efficiency of human developers.

Code Generation

Vulnerability Detection with Code Language Models: How Far Are We?

1 code implementation27 Mar 2024 Yangruibo Ding, Yanjun Fu, Omniyyah Ibrahim, Chawin Sitawarin, Xinyun Chen, Basel Alomair, David Wagner, Baishakhi Ray, Yizheng Chen

Evaluating code LMs on PrimeVul reveals that existing benchmarks significantly overestimate the performance of these models.

Vulnerability Detection

Code-Aware Prompting: A study of Coverage Guided Test Generation in Regression Setting using LLM

no code implementations31 Jan 2024 Gabriel Ryan, Siddhartha Jain, Mingyue Shang, Shiqi Wang, Xiaofei Ma, Murali Krishna Ramanathan, Baishakhi Ray

Recent works using large language models (LLMs) for test generation have focused on improving generation quality through optimizing the test generation context and correcting errors in model outputs, but use fixed prompting strategies that prompt the model to generate tests without additional guidance.

Beyond Accuracy: Evaluating Self-Consistency of Code Large Language Models with IdentityChain

1 code implementation21 Oct 2023 Marcus J. Min, Yangruibo Ding, Luca Buratti, Saurabh Pujar, Gail Kaiser, Suman Jana, Baishakhi Ray

In this paper, we first formally define the self-consistency of Code LLMs and then design a framework, IdentityChain, which effectively and efficiently evaluates the self-consistency and conventional accuracy of a model at the same time.

Code Generation Code Summarization

Towards Causal Deep Learning for Vulnerability Detection

no code implementations12 Oct 2023 Md Mahbubur Rahman, Ira Ceka, Chengzhi Mao, Saikat Chakraborty, Baishakhi Ray, Wei Le

Our results show that CausalVul consistently improved the model accuracy, robustness and OOD performance for all the state-of-the-art models and datasets we experimented.

Vulnerability Detection

Language-Guided Traffic Simulation via Scene-Level Diffusion

no code implementations10 Jun 2023 Ziyuan Zhong, Davis Rempe, Yuxiao Chen, Boris Ivanovic, Yulong Cao, Danfei Xu, Marco Pavone, Baishakhi Ray

Realistic and controllable traffic simulation is a core capability that is necessary to accelerate autonomous vehicle (AV) development.

Language Modelling Large Language Model

Variation of Gender Biases in Visual Recognition Models Before and After Finetuning

no code implementations14 Mar 2023 Jaspreet Ranjit, Tianlu Wang, Baishakhi Ray, Vicente Ordonez

We also find that (2) models finetuned on larger scale datasets are more likely to introduce new biased associations.

Object Recognition

On ML-Based Program Translation: Perils and Promises

1 code implementation21 Feb 2023 Aniketh Malyala, Katelyn Zhou, Baishakhi Ray, Saikat Chakraborty

In the future, we envision an end-to-end program translation tool where programming domain knowledge can be embedded into an ML-based translation pipeline using pre- and post-processing steps.

Translation

ReCode: Robustness Evaluation of Code Generation Models

2 code implementations20 Dec 2022 Shiqi Wang, Zheng Li, Haifeng Qian, Chenghao Yang, Zijian Wang, Mingyue Shang, Varun Kumar, Samson Tan, Baishakhi Ray, Parminder Bhatia, Ramesh Nallapati, Murali Krishna Ramanathan, Dan Roth, Bing Xiang

Most existing works on robustness in text or code tasks have focused on classification, while robustness in generation tasks is an uncharted area and to date there is no comprehensive benchmark for robustness in code generation.

Code Generation

Multi-lingual Evaluation of Code Generation Models

2 code implementations26 Oct 2022 Ben Athiwaratkun, Sanjay Krishna Gouda, Zijian Wang, Xiaopeng Li, Yuchen Tian, Ming Tan, Wasi Uddin Ahmad, Shiqi Wang, Qing Sun, Mingyue Shang, Sujan Kumar Gonugondla, Hantian Ding, Varun Kumar, Nathan Fulton, Arash Farahani, Siddhartha Jain, Robert Giaquinto, Haifeng Qian, Murali Krishna Ramanathan, Ramesh Nallapati, Baishakhi Ray, Parminder Bhatia, Sudipta Sengupta, Dan Roth, Bing Xiang

Using these benchmarks, we are able to assess the performance of code generation models in a multi-lingual fashion, and discovered generalization ability of language models on out-of-domain languages, advantages of multi-lingual models over mono-lingual, the ability of few-shot prompting to teach the model new languages, and zero-shot translation abilities even on mono-lingual settings.

Code Completion Code Translation +1

NatGen: Generative pre-training by "Naturalizing" source code

1 code implementation15 Jun 2022 Saikat Chakraborty, Toufique Ahmed, Yangruibo Ding, Premkumar Devanbu, Baishakhi Ray

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 translation.

Code Translation Few-Shot Learning +1

Repairing Group-Level Errors for DNNs Using Weighted Regularization

1 code implementation24 Mar 2022 Ziyuan Zhong, Yuchi Tian, Conor J. Sweeney, Vicente Ordonez, Baishakhi Ray

In particular, it can repair confusion error and bias error of DNN models for both single-label and multi-label image classifications.

Unicorn: Reasoning about Configurable System Performance through the lens of Causality

1 code implementation20 Jan 2022 Md Shahriar Iqbal, Rahul Krishna, Mohammad Ali Javidian, Baishakhi Ray, Pooyan Jamshidi

Understanding and reasoning about the performance behavior of highly configurable systems, over a vast and variable space, is challenging.

BIG-bench Machine Learning Causal Inference +1

VELVET: a noVel Ensemble Learning approach to automatically locate VulnErable sTatements

1 code implementation20 Dec 2021 Yangruibo Ding, Sahil Suneja, Yunhui Zheng, Jim Laredo, Alessandro Morari, Gail Kaiser, Baishakhi Ray

Automatically locating vulnerable statements in source code is crucial to assure software security and alleviate developers' debugging efforts.

Ensemble Learning

A Survey on Scenario-Based Testing for Automated Driving Systems in High-Fidelity Simulation

no code implementations2 Dec 2021 Ziyuan Zhong, Yun Tang, Yuan Zhou, Vania de Oliveira Neves, Yang Liu, Baishakhi Ray

To bridge this gap, in this work, we provide a generic formulation of scenario-based testing in high-fidelity simulation and conduct a literature review on the existing works.

Towards Learning (Dis)-Similarity of Source Code from Program Contrasts

no code implementations ACL 2022 Yangruibo Ding, Luca Buratti, Saurabh Pujar, Alessandro Morari, Baishakhi Ray, Saikat Chakraborty

We pre-train our model with a much smaller dataset, the size of which is only 5% of the state-of-the-art models' training datasets, to illustrate the effectiveness of our data augmentation and the pre-training approach.

Clone Detection Contrastive Learning +2

Detecting Multi-Sensor Fusion Errors in Advanced Driver-Assistance Systems

3 code implementations14 Sep 2021 Ziyuan Zhong, Zhisheng Hu, Shengjian Guo, Xinyang Zhang, Zhenyu Zhong, Baishakhi Ray

We define the failures (e. g., car crashes) caused by the faulty MSF as fusion errors and develop a novel evolutionary-based domain-specific search framework, FusED, for the efficient detection of fusion errors.

Autonomous Driving Sensor Fusion

Neural Network Guided Evolutionary Fuzzing for Finding Traffic Violations of Autonomous Vehicles

1 code implementation13 Sep 2021 Ziyuan Zhong, Gail Kaiser, Baishakhi Ray

Self-driving cars and trucks, autonomous vehicles (AVs), should not be accepted by regulatory bodies and the public until they have much higher confidence in their safety and reliability -- which can most practically and convincingly be achieved by testing.

Self-Driving Cars

Retrieval Augmented Code Generation and Summarization

1 code implementation Findings (EMNLP) 2021 Md Rizwan Parvez, Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang

To mimic developers' code or summary generation behavior, we propose a retrieval augmented framework, REDCODER, that retrieves relevant code or summaries from a retrieval database and provides them as a supplement to code generation or summarization models.

 Ranked #1 on Code Generation on CodeXGLUE - CodeSearchNet (using extra training data)

Code Generation Code Summarization +1

On Multi-Modal Learning of Editing Source Code

1 code implementation15 Aug 2021 Saikat Chakraborty, Baishakhi Ray

With in-depth investigation and analysis, we show that developers' hint as an input modality can narrow the search space for patches and outperform state-of-the-art models to generate correctly patched code in top-1 position.

NMT

Unified Pre-training for Program Understanding and Generation

1 code implementation NAACL 2021 Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang

Experiments on code summarization in the English language, code generation, and code translation in seven programming languages show that PLBART outperforms or rivals state-of-the-art models.

Clone Detection Code Summarization +6

Trex: Learning Execution Semantics from Micro-Traces for Binary Similarity

2 code implementations16 Dec 2020 Kexin Pei, Zhou Xuan, Junfeng Yang, Suman Jana, Baishakhi Ray

We thus train the model to learn execution semantics from the functions' micro-traces, without any manual labeling effort.

Transfer Learning Vulnerability Detection

Understanding Local Robustness of Deep Neural Networks under Natural Variations

1 code implementation9 Oct 2020 Ziyuan Zhong, Yuchi Tian, Baishakhi Ray

To this end, we study the local per-input robustness properties of the DNNs and leverage those properties to build a white-box (DeepRobust-W) and a black-box (DeepRobust-B) tool to automatically identify the non-robust points.

Autonomous Driving Image Classification

Deep Learning & Software Engineering: State of Research and Future Directions

1 code implementation17 Sep 2020 Prem Devanbu, Matthew Dwyer, Sebastian Elbaum, Michael Lowry, Kevin Moran, Denys Poshyvanyk, Baishakhi Ray, Rishabh Singh, Xiangyu Zhang

The intent of this report is to serve as a potential roadmap to guide future work that sits at the intersection of SE & DL.

Deep Learning based Vulnerability Detection: Are We There Yet?

1 code implementation3 Sep 2020 Saikat Chakraborty, Rahul Krishna, Yangruibo Ding, Baishakhi Ray

In this paper, we ask, "how well do the state-of-the-art DL-based techniques perform in a real-world vulnerability prediction scenario?".

Software Engineering

Patching as Translation: the Data and the Metaphor

1 code implementation24 Aug 2020 Yangruibo Ding, Baishakhi Ray, Premkumar Devanbu, Vincent J. Hellendoorn

Given these findings, we demonstrate how a more principled approach to model design, based on our empirical findings and general knowledge of software development, can lead to better solutions.

General Knowledge Program Repair +1

Multitask Learning Strengthens Adversarial Robustness

1 code implementation ECCV 2020 Chengzhi Mao, Amogh Gupta, Vikram Nitin, Baishakhi Ray, Shuran Song, Junfeng Yang, Carl Vondrick

Although deep networks achieve strong accuracy on a range of computer vision benchmarks, they remain vulnerable to adversarial attacks, where imperceptible input perturbations fool the network.

Adversarial Defense Adversarial Robustness

MTFuzz: Fuzzing with a Multi-Task Neural Network

1 code implementation25 May 2020 Dongdong She, Rahul Krishna, Lu Yan, Suman Jana, Baishakhi Ray

The compact embedding can be used to guide the mutation process effectively by focusing most of the mutations on the parts of the embedding where the gradient is high.

Software Engineering

Pythia: Grammar-Based Fuzzing of REST APIs with Coverage-guided Feedback and Learning-based Mutations

no code implementations23 May 2020 Vaggelis Atlidakis, Roxana Geambasu, Patrice Godefroid, Marina Polishchuk, Baishakhi Ray

This paper introduces Pythia, the first fuzzer that augments grammar-based fuzzing with coverage-guided feedback and a learning-based mutation strategy for stateful REST API fuzzing.

valid

ConEx: Efficient Exploration of Big-Data System Configurations for Better Performance

3 code implementations17 Oct 2019 Rahul Krishna, Chong Tang, Kevin Sullivan, Baishakhi Ray

For cost reduction, we developed and experimentally tested and validated two approaches: using scaled-up big data jobs as proxies for the objective function for larger jobs and using a dynamic job similarity measure to infer that results obtained for one kind of big data problem will work well for similar problems.

Efficient Exploration

AdvSPADE: Realistic Unrestricted Attacks for Semantic Segmentation

no code implementations6 Oct 2019 Guangyu Shen, Chengzhi Mao, Junfeng Yang, Baishakhi Ray

Due to the inherent robustness of segmentation models, traditional norm-bounded attack methods show limited effect on such type of models.

Adversarial Attack Segmentation +1

Neutaint: Efficient Dynamic Taint Analysis with Neural Networks

no code implementations8 Jul 2019 Dongdong She, Yizheng Chen, Baishakhi Ray, Suman Jana

Dynamic taint analysis (DTA) is widely used by various applications to track information flow during runtime execution.

Cryptography and Security

Testing DNN Image Classifiers for Confusion & Bias Errors

1 code implementation20 May 2019 Yuchi Tian, Ziyuan Zhong, Vicente Ordonez, Gail Kaiser, Baishakhi Ray

We found that many of the reported erroneous cases in popular DNN image classifiers occur because the trained models confuse one class with another or show biases towards some classes over others.

Avg DNN Testing +2

Tree2Tree Neural Translation Model for Learning Source Code Changes

no code implementations30 Sep 2018 Saikat Chakraborty, Miltiadis Allamanis, Baishakhi Ray

Our evaluation shows the effectiveness of CODIT in learning and suggesting abstract change templates.

Software Engineering

A Case Study on the Impact of Similarity Measure on Information Retrieval based Software Engineering Tasks

no code implementations8 Aug 2018 Md Masudur Rahman, Saikat Chakraborty, Gail Kaiser, Baishakhi Ray

In particular, we analyze two previously proposed tools for project recommendation and bug localization tasks, which leverage diverse software artifacts, and observe that an informed choice of similarity measure indeed leads to improved performance of the existing SE tools.

Information Retrieval Retrieval

NEUZZ: Efficient Fuzzing with Neural Program Smoothing

1 code implementation15 Jul 2018 Dongdong She, Kexin Pei, Dave Epstein, Junfeng Yang, Baishakhi Ray, Suman Jana

However, even state-of-the-art fuzzers are not very efficient at finding hard-to-trigger software bugs.

Evolutionary Algorithms

Obfuscation Resilient Search through Executable Classification

1 code implementation6 Jun 2018 Fang-Hsiang Su, Jonathan Bell, Gail Kaiser, Baishakhi Ray

It is challenging to search for executables relevant to an obfuscated application for developers to analyze efficiently.

Software Engineering Cryptography and Security

DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars

1 code implementation28 Aug 2017 Yuchi Tian, Kexin Pei, Suman Jana, Baishakhi Ray

Most existing testing techniques for DNN-driven vehicles are heavily dependent on the manual collection of test data under different driving conditions which become prohibitively expensive as the number of test conditions increases.

Autonomous Vehicles

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