Search Results for author:
Found 52 papers, 30 papers with code
APIRecX: Cross-Library API Recommendation via Pre-Trained Language Model
APIRecX can migrate the knowledge of existing libraries to a new library, and can recommend APIs that are previously regarded as OOV.
Automated Data Visualization from Natural Language via Large Language Models: An Exploratory Study
In particular, we first explore the ways of transforming structured tabular data into sequential text prompts, as to feed them into LLMs and analyze which table content contributes most to the NL2Vis.
Graph Neural Networks for Vulnerability Detection: A Counterfactual Explanation
We argue that these factual reasoning-based explanations cannot answer critical what-if questions: What would happen to the GNN's decision if we were to alter the code graph into alternative structures?
CodeIP: A Grammar-Guided Multi-Bit Watermark for Large Language Models of Code
As Large Language Models (LLMs) are increasingly used to automate code generation, it is often desired to know if the code is AI-generated and by which model, especially for purposes like protecting intellectual property (IP) in industry and preventing academic misconduct in education.
Does Your Neural Code Completion Model Use My Code? A Membership Inference Approach
Subsequently, the membership classifier can be effectively employed to deduce the membership status of a given code sample based on the output of a target code completion model.
VISION2UI: A Real-World Dataset with Layout for Code Generation from UI Designs
Automatically generating UI code from webpage design visions can significantly alleviate the burden of developers, enabling beginner developers or designers to directly generate Web pages from design diagrams.
Iterative Refinement of Project-Level Code Context for Precise Code Generation with Compiler Feedback
Large language models (LLMs) have shown remarkable progress in automated code generation.
FedHCDR: Federated Cross-Domain Recommendation with Hypergraph Signal Decoupling
Specifically, to address the data heterogeneity across domains, we introduce an approach called hypergraph signal decoupling (HSD) to decouple the user features into domain-exclusive and domain-shared features.
NL2Formula: Generating Spreadsheet Formulas from Natural Language Queries
Writing formulas on spreadsheets, such as Microsoft Excel and Google Sheets, is a widespread practice among users performing data analysis.
High-dimensional Bayesian Optimization via Covariance Matrix Adaptation Strategy
To address this issue, a promising solution is to use a local search strategy that partitions the search domain into local regions with high likelihood of containing the global optimum, and then use BO to optimize the objective function within these regions.
On the Semantics of LM Latent Space: A Vocabulary-defined Approach
In response, we introduce a pioneering method called vocabulary-defined semantics, which establishes a reference frame within the LM latent space, ensuring disentangled semantic analysis grounded in LM vocabulary.
KADEL: Knowledge-Aware Denoising Learning for Commit Message Generation
However, previous methods are trained on the entire dataset without considering the fact that a portion of commit messages adhere to good practice (i. e., good-practice commits), while the rest do not.
Between Lines of Code: Unraveling the Distinct Patterns of Machine and Human Programmers
Based on our findings, we propose DetectCodeGPT, a novel method for detecting machine-generated code, which improves DetectGPT by capturing the distinct stylized patterns of code.
Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit
We also benchmark several state-of-the-art neural models for code intelligence, and provide an open-source toolkit tailored for the rapid prototyping of deep-learning-based code intelligence models.
Neuron-level LLM Patching for Code Generation
In this paper, we propose a novel and effective model editing approach, \textsc{MENT}, to patch LLMs in coding tasks.
FedDCSR: Federated Cross-domain Sequential Recommendation via Disentangled Representation Learning
Nonetheless, the sequence feature heterogeneity across different domains significantly impacts the overall performance of FL.
DGSD: Dynamical Graph Self-Distillation for EEG-Based Auditory Spatial Attention Detection
Specifically, to effectively represent the non-Euclidean properties of EEG signals, dynamical graph convolutional networks are applied to represent the graph structure of EEG signals, which can also extract crucial features related to auditory spatial attention in EEG signals.
SoTaNa: The Open-Source Software Development Assistant
To meet the demands of this dynamic field, there is a growing need for an effective software development assistant.
Try with Simpler -- An Evaluation of Improved Principal Component Analysis in Log-based Anomaly Detection
This addresses the issue of unseen log events in training data, enhancing log representation.
Decarbonizing the European energy system in the absence of Russian gas: Hydrogen uptake and carbon capture developments in the power, heat and industry sectors
Hydrogen and carbon capture and storage are pivotal to decarbonize the European energy system in a broad range of pathway scenarios.
Modularizing while Training: A New Paradigm for Modularizing DNN Models
In this paper, we propose a novel approach that incorporates modularization into the model training process, i. e., modularizing-while-training (MwT).
Provably Efficient Bayesian Optimization with Unbiased Gaussian Process Hyperparameter Estimation
Our method uses a multi-armed bandit technique (EXP3) to add random data points to the BO process, and employs a novel training loss function for the GP hyperparameter estimation process that ensures unbiased estimation from the observed data.
Log Parsing: How Far Can ChatGPT Go?
Our results show that ChatGPT can achieve promising results for log parsing with appropriate prompts, especially with few-shot prompting.
Towards Efficient Fine-tuning of Pre-trained Code Models: An Experimental Study and Beyond
Our experimental study shows that (1) lexical, syntactic and structural properties of source code are encoded in the lower, intermediate, and higher layers, respectively, while the semantic property spans across the entire model.
Reusing Deep Neural Network Models through Model Re-engineering
Prior approaches to DNN model reuse have two main limitations: 1) reusing the entire model, while only a small part of the model's functionalities (labels) are required, would cause much overhead (e. g., computational and time costs for inference), and 2) model reuse would inherit the defects and weaknesses of the reused model, and hence put the new system under threats of security attack.
xASTNN: Improved Code Representations for Industrial Practice
The proposed xASTNN has three advantages.
Uncertainty-Aware Performance Prediction for Highly Configurable Software Systems via Bayesian Neural Networks
We also develop a novel uncertainty calibration technique to ensure the reliability of the confidence intervals generated by a Bayesian prediction model.
Automatic Semantic Modeling for Structural Data Source with the Prior Knowledge from Knowledge Base
Therefore, an automatic approach for learning the semantics of a data source is desirable.
Exploring Representation-Level Augmentation for Code Search
In this paper, we explore augmentation methods that augment data (both code and query) at representation level which does not require additional data processing and training, and based on this we propose a general format of representation-level augmentation that unifies existing methods.
Enhanced Fairness Testing via Generating Effective Initial Individual Discriminatory Instances
In this paper, we propose an approach for the selection of the initial seeds to generate IDIs for fairness testing.
LogGD:Detecting Anomalies from System Logs by Graph Neural Networks
They usually take log event counts or sequential log events as inputs and utilize machine learning algorithms including deep learning models to detect system anomalies.
Patching Weak Convolutional Neural Network Models through Modularization and Composition
To patch a weak CNN model that performs unsatisfactorily on a target class (TC), we compose the weak CNN model with the corresponding module obtained from a strong CNN model.
No More Fine-Tuning? An Experimental Evaluation of Prompt Tuning in Code Intelligence
Besides, the performance of fine-tuning strongly relies on the amount of downstream data, while in practice, the scenarios with scarce data are common.
CoCoSoDa: Effective Contrastive Learning for Code Search
However, there is still a lot of room for improvement in using contrastive learning for code search.
Accelerating Code Search with Deep Hashing and Code Classification
Code search is to search reusable code snippets from source code corpus based on natural languages queries.
RACE: Retrieval-Augmented Commit Message Generation
Furthermore, RACE can boost the performance of existing Seq2Seq models in commit message generation.
What Do They Capture? -- A Structural Analysis of Pre-Trained Language Models for Source Code
In this paper, we conduct a thorough structural analysis aiming to provide an interpretation of pre-trained language models for source code (e. g., CodeBERT, and GraphCodeBERT) from three distinctive perspectives: (1) attention analysis, (2) probing on the word embedding, and (3) syntax tree induction.
Log-based Anomaly Detection with Deep Learning: How Far Are We?
Recently, many deep learning models have been proposed to automatically detect system anomalies based on log data.
Cross-Language Binary-Source Code Matching with Intermediate Representations
Binary-source code matching plays an important role in many security and software engineering related tasks such as malware detection, reverse engineering and vulnerability assessment.
Graph-based Incident Aggregation for Large-Scale Online Service Systems
The proposed framework is evaluated with real-world incident data collected from a large-scale online service system of Huawei Cloud.
Log-based Anomaly Detection Without Log Parsing
The log parsing errors could cause the loss of important information for anomaly detection.
On the Evaluation of Neural Code Summarization
To achieve a profound understanding of how far we are from solving this problem and provide suggestions to future research, in this paper, we conduct a systematic and in-depth analysis of 5 state-of-the-art neural code summarization models on 6 widely used BLEU variants, 4 pre-processing operations and their combinations, and 3 widely used datasets.
On the Evaluation of Commit Message Generation Models: An Experimental Study
We find that: (1) Different variants of the BLEU metric are used in previous works, which affects the evaluation and understanding of existing methods.
Embedding API Dependency Graph for Neural Code Generation
In recent years, many deep learning based approaches have been proposed, which can generate a sequence of code from a sequence of textual program description.
A New Look and Convergence Rate of Federated Multi-Task Learning with Laplacian Regularization
Non-Independent and Identically Distributed (non- IID) data distribution among clients is considered as the key factor that degrades the performance of federated learning (FL).
Confused Modulo Projection based Somewhat Homomorphic Encryption -- Cryptosystem, Library and Applications on Secure Smart Cities
To make this algorithm easy to use, we also designed and implemented an efficient general blind computing library based on CMP-SWHE.
Language Modelling for Source Code with Transformer-XL
It has been found that software, like natural language texts, exhibits "naturalness", which can be captured by statistical language models.
Is Attention All What You Need? -- An Empirical Investigation on Convolution-Based Active Memory and Self-Attention
The key to a Transformer model is the self-attention mechanism, which allows the model to analyze an entire sequence in a computationally efficient manner.
Cost-Effective Testing of a Deep Learning Model through Input Reduction
Experiments with various DL models and datasets show that our approach can reduce the whole testing data to 4. 6\% on average, and can reliably estimate the performance of DL models.
DeepAM: Migrate APIs with Multi-modal Sequence to Sequence Learning
They rely on the sparse availability of bilingual projects, thus producing a limited number of API mappings.
Neural Programming by Example
In this paper, we propose a deep neural networks (DNN) based PBE model called Neural Programming by Example (NPBE), which can learn from input-output strings and induce programs that solve the string manipulation problems.
Deep API Learning
We propose DeepAPI, a deep learning based approach to generate API usage sequences for a given natural language query.
