Papers Code Classification
“Code Classification” 태그가 달린 논문 39편 · 필터 해제
Federated Learning for ICD Classification with Lightweight Models and Pretrained Embeddings
This study investigates the feasibility and performance of federated learning (FL) for multi-label ICD code classification using clinical notes from the MIMIC-IV dataset. Unlike previous approaches that rely on centraliz…
Code ClassificationFederated LearningPrivacy PreservingCASTLE: Benchmarking Dataset for Static Code Analyzers and LLMs towards CWE Detection
Identifying vulnerabilities in source code is crucial, especially in critical software components. Existing methods such as static analysis, dynamic analysis, formal verification, and recently Large Language Models are w…
BenchmarkingCode ClassificationCode CompletionVulnerability DetectionORIGAMI: A generative transformer architecture for predictions from semi-structured data
Despite the popularity and widespread use of semi-structured data formats such as JSON, end-to-end supervised learning applied directly to such data remains underexplored. We present ORIGAMI (Object RepresentatIon via Ge…
Code ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONvalidLarge Language Model in Medical Informatics: Direct Classification and Enhanced Text Representations for Automatic ICD Coding
Addressing the complexity of accurately classifying International Classification of Diseases (ICD) codes from medical discharge summaries is challenging due to the intricate nature of medical documentation. This paper ex…
ClassificationCode ClassificationLanguage ModelingLanguage Modelling+1More Questions than Answers? Lessons from Integrating Explainable AI into a Cyber-AI Tool
We share observations and challenges from an ongoing effort to implement Explainable AI (XAI) in a domain-specific workflow for cybersecurity analysts. Specifically, we briefly describe a preliminary case study on the us…
Code ClassificationEnhancing Source Code Classification Effectiveness via Prompt Learning Incorporating Knowledge Features
Researchers have investigated the potential of leveraging pre-trained language models, such as CodeBERT, to enhance source code-related tasks. Previous methodologies have relied on CodeBERT's '[CLS]' token as the embeddi…
ClassificationCode ClassificationLanguage ModellingPrompt Learning+5Graph Convolutions Enrich the Self-Attention in Transformers!
Transformers, renowned for their self-attention mechanism, have achieved state-of-the-art performance across various tasks in natural language processing, computer vision, time-series modeling, etc. However, one of the c…
Clone DetectionCode ClassificationDefect Detection+5Sparse Attention-Based Neural Networks for Code Classification
Categorizing source codes accurately and efficiently is a challenging problem in real-world programming education platform management. In recent years, model-based approaches utilizing abstract syntax trees (ASTs) have b…
ClassificationCode ClassificationA General-Purpose Self-Supervised Model for Computational Pathology
Tissue phenotyping is a fundamental computational pathology (CPath) task in learning objective characterizations of histopathologic biomarkers in anatomic pathology. However, whole-slide imaging (WSI) poses a complex com…
Code ClassificationDiagnosticRepresentation LearningTransfer LearningReplication and Extension of Schnappinger’s Study on Human-level Ordinal Maintainability Prediction Based on Static Code Metrics
As a part of a research project concerning software maintainability assessment in collaboration with the development team, we wanted to explore dissensions between developers and the confounding effect of size. To this e…
Code ClassificationUnderstanding Programs by Exploiting (Fuzzing) Test Cases
Semantic understanding of programs has attracted great attention in the community. Inspired by recent successes of large language models (LLMs) in natural language understanding, tremendous progress has been made by trea…
Clone DetectionCode ClassificationNatural Language UnderstandingRepresentation LearningInProC: Industry and Product/Service Code Classification
Determining industry and product/service codes for a company is an important real-world task and is typically very expensive as it involves manual curation of data about the companies. Building an AI agent that can predi…
AI AgentClassificationCode ClassificationRepresentation LearningThe EarlyBIRD Catches the Bug: On Exploiting Early Layers of Encoder Models for More Efficient Code Classification
The use of modern Natural Language Processing (NLP) techniques has shown to be beneficial for software engineering tasks, such as vulnerability detection and type inference. However, training deep NLP models requires sig…
Code ClassificationDefect DetectionMulti-class ClassificationVulnerability DetectionHeterogeneous Directed Hypergraph Neural Network over abstract syntax tree (AST) for Code Classification
Code classification is a difficult issue in program understanding and automatic coding. Due to the elusive syntax and complicated semantics in programs, most existing studies use techniques based on abstract syntax tree …
Code ClassificationGraph Neural NetworkxASTNN: Improved Code Representations for Industrial Practice
The application of deep learning techniques in software engineering becomes increasingly popular. One key problem is developing high-quality and easy-to-use source code representations for code-related tasks. The researc…
Clone DetectionCode ClassificationBoosting Source Code Learning with Text-Oriented Data Augmentation: An Empirical Study
Recent studies have demonstrated remarkable advancements in source code learning, which applies deep neural networks (DNNs) to tackle various software engineering tasks. Similar to other DNN-based domains, source code le…
Clone DetectionCode ClassificationData AugmentationMIXCODE: Enhancing Code Classification by Mixup-Based Data Augmentation
Inspired by the great success of Deep Neural Networks (DNNs) in natural language processing (NLP), DNNs have been increasingly applied in source code analysis and attracted significant attention from the software enginee…
ClassificationCode ClassificationData AugmentationOn the Effectiveness of Hybrid Pooling in Mixup-Based Graph Learning for Language Processing
Graph neural network (GNN)-based graph learning has been popular in natural language and programming language processing, particularly in text and source code classification. Typically, GNNs are constructed by incorporat…
Code ClassificationData AugmentationGraph ClassificationGraph Learning+1Adding Context to Source Code Representations for Deep Learning
Deep learning models have been successfully applied to a variety of software engineering tasks, such as code classification, summarisation, and bug and vulnerability detection. In order to apply deep learning to these ta…
Code ClassificationDeep LearningVulnerability DetectionCodeS: Towards Code Model Generalization Under Distribution Shift
Distribution shift has been a longstanding challenge for the reliable deployment of deep learning (DL) models due to unexpected accuracy degradation. Although DL has been becoming a driving force for large-scale source c…
BenchmarkingCode Classificationmodel