Search Results for author:
Found 28 papers, 17 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.
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.
