Search Results for author: Nghi D. Q. Bui

Found 11 papers, 3 papers with code

Detect-Localize-Repair: A Unified Framework for Learning to Debug with CodeT5

no code implementations27 Nov 2022 Nghi D. Q. Bui, Yue Wang, Steven Hoi

Specifically, we propose three objectives to adapt the generic CodeT5 for debugging: a bug detection objective to determine whether a given code snippet is buggy or not, a bug localization objective to identify the buggy lines, and a program repair objective to translate the buggy code to its fixed version.

Language Modelling Program Repair

Learning to Represent Programs with Code Hierarchies

no code implementations31 May 2022 Minh H. Nguyen, Nghi D. Q. Bui, Truong Son Hy, Long Tran-Thanh, Risi Kondor

To address these issues, we propose a method for representing code as a hierarchy (Code Hierarchy), in which different code components are represented separately at various levels of granularity.

Clone Detection Code Classification +1

Towards Using Data-Influence Methods to Detect Noisy Samples in Source Code Corpora

no code implementations25 May 2022 Anh T. V. Dau, Thang Nguyen-Duc, Hoang Thanh-Tung, Nghi D. Q. Bui

Despite the recent trend of developing and applying neural source code models to software engineering tasks, the quality of such models is insufficient for real-world use.

Code Classification Representation Learning

Energy-bounded Learning for Robust Models of Code

no code implementations20 Dec 2021 Nghi D. Q. Bui, Yijun Yu

In programming, learning code representations has a variety of applications, including code classification, code search, comment generation, bug prediction, and so on.

Code Classification Code Search +1

InferCode: Self-Supervised Learning of Code Representations by Predicting Subtrees

no code implementations13 Dec 2020 Nghi D. Q. Bui, Yijun Yu, Lingxiao Jiang

We trained an InferCode model instance using the Tree-based CNN as the encoder of a large set of Java code and applied it to downstream unsupervised tasks such as code clustering, code clone detection, cross-language code search or reused under a transfer learning scheme to continue training the model weights for supervised tasks such as code classification and method name prediction.

Clone Detection Code Classification +6

TreeCaps: Tree-Based Capsule Networks for Source Code Processing

no code implementations5 Sep 2020 Nghi D. Q. Bui, Yijun Yu, Lingxiao Jiang

Although syntax trees are precisely defined according to the language grammar and easier to construct and process than graphs, previous tree-based learning techniques have not been able to learn semantic information from trees to achieve better accuracy than graph-based techniques.

On the Generalizability of Neural Program Models with respect to Semantic-Preserving Program Transformations

1 code implementation31 Jul 2020 Md Rafiqul Islam Rabin, Nghi D. Q. Bui, Ke Wang, Yijun Yu, Lingxiao Jiang, Mohammad Amin Alipour

With the prevalence of publicly available source code repositories to train deep neural network models, neural program models can do well in source code analysis tasks such as predicting method names in given programs that cannot be easily done by traditional program analysis techniques.

Method name prediction

SAR: Learning Cross-Language API Mappings with Little Knowledge

no code implementations10 Jun 2019 Nghi D. Q. Bui, Yijun Yu, Lingxiao Jiang

However, all these approaches still require large amount of manual effort in preparing parallel program corpora, ranging from pairs of APIs, to manually identified code in different languages that are considered as functionally equivalent.

Domain Adaptation Translation

Hierarchical Learning of Cross-Language Mappings through Distributed Vector Representations for Code

1 code implementation13 Mar 2018 Nghi D. Q. Bui, Lingxiao Jiang

Our preliminary evaluations on about 40, 000 Java and C# source files from 9 software projects show that our approach can automatically learn shared embeddings for various code elements in different languages and identify their cross-language mappings with reasonable Mean Average Precision scores.

Translation Word Embeddings

Cross-Language Learning for Program Classification using Bilateral Tree-Based Convolutional Neural Networks

1 code implementation17 Oct 2017 Nghi D. Q. Bui, Lingxiao Jiang, Yijun Yu

It is layered on top of two tree-based convolutional neural networks (TBCNNs), each of which recognizes the algorithm of code written in an individual programming language.

Classification General Classification

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