{"total":69,"rows":[{"id":"2109.00859","title":"CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation","created":"2021-09-02","cited_by_count":906,"area":null},{"id":"2102.04664","title":"CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation","created":"2021-02-09","cited_by_count":744,"area":null},{"id":"2308.10620","title":"Large Language Models for Software Engineering: A Systematic Literature Review","created":"2023-08-21","cited_by_count":399,"area":null},{"id":"2103.06333","title":"Unified Pre-training for Program Understanding and Generation","created":"2021-03-10","cited_by_count":366,"area":null},{"id":"2102.07492","title":"DOBF: A Deobfuscation Pre-Training Objective for Programming Languages","created":"2021-02-15","cited_by_count":68,"area":null},{"id":"2104.09340","title":"Code Structure Guided Transformer for Source Code Summarization","created":"2021-04-19","cited_by_count":30,"area":null},{"id":"2112.02268","title":"Bridging Pre-trained Models and Downstream Tasks for Source Code Understanding","created":"2021-12-04","cited_by_count":28,"area":null},{"id":"2105.04297","title":"How could Neural Networks understand Programs?","created":"2021-05-10","cited_by_count":26,"area":null},{"id":"2108.06645","title":"On Multi-Modal Learning of Editing Source Code","created":"2021-08-15","cited_by_count":25,"area":null},{"id":"2401.00288","title":"Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit","created":"2023-12-30","cited_by_count":22,"area":null},{"id":"2205.11739","title":"Deep Learning Meets Software Engineering: A Survey on Pre-Trained Models of Source Code","created":"2022-05-24","cited_by_count":21,"area":null},{"id":"2409.12699","title":"PromSec: Prompt Optimization for Secure Generation of Functional Source Code with Large Language Models (LLMs)","created":"2024-09-19","cited_by_count":19,"area":null},{"id":"2107.09130","title":"GNN4IP: Graph Neural Network for Hardware Intellectual Property Piracy Detection","created":"2021-07-19","cited_by_count":14,"area":null},{"id":"2210.17029","title":"Poison Attack and Defense on Deep Source Code Processing Models","created":"2022-10-31","cited_by_count":11,"area":"safety"},{"id":"2312.04234","title":"Graph Convolutions Enrich the Self-Attention in Transformers!","created":"2023-12-07","cited_by_count":11,"area":null},{"id":"2408.05715","title":"Top Pass: Improve Code Generation by Pass@k-Maximized Code Ranking","created":"2024-08-11","cited_by_count":9,"area":null},{"id":"2311.07553","title":"An Extensive Study on Adversarial Attack against Pre-trained Models of Code","created":"2023-11-13","cited_by_count":8,"area":null},{"id":"2203.11790","title":"Learning Program Semantics with Code Representations: An Empirical Study","created":"2022-03-22","cited_by_count":6,"area":null},{"id":"2208.08643","title":"Learning Program Representations with a Tree-Structured Transformer","created":"2022-08-18","cited_by_count":6,"area":null},{"id":"2210.04633","title":"CAT-probing: A Metric-based Approach to Interpret How Pre-trained Models for Programming Language Attend Code Structure","created":"2022-10-07","cited_by_count":6,"area":null},{"id":"2212.05901","title":"Parameter-Efficient Finetuning of Transformers for Source Code","created":"2022-12-12","cited_by_count":6,"area":null},{"id":"2212.05976","title":"DexBERT: Effective, Task-Agnostic and Fine-grained Representation Learning of Android Bytecode","created":"2022-12-12","cited_by_count":6,"area":null},{"id":"2504.19444","title":"Large Language Models are Qualified Benchmark Builders: Rebuilding Pre-Training Datasets for Advancing Code Intelligence Tasks","created":"2025-04-28","cited_by_count":6,"area":null},{"id":"2103.15361","title":"Embedding API Dependency Graph for Neural Code Generation","created":"2021-03-29","cited_by_count":5,"area":null},{"id":"2206.04730","title":"A Neural Network Architecture for Program Understanding Inspired by Human Behaviors","created":"2022-05-10","cited_by_count":5,"area":null},{"id":"2208.11947","title":"TEP-GNN: Accurate Execution Time Prediction of Functional Tests using Graph Neural Networks","created":"2022-08-25","cited_by_count":5,"area":null},{"id":"2309.10264","title":"Revisiting and Improving Retrieval-Augmented Deep Assertion Generation","created":"2023-09-19","cited_by_count":5,"area":"rag"},{"id":"2208.12588","title":"Generalizability of Code Clone Detection on CodeBERT","created":"2022-08-26","cited_by_count":4,"area":null},{"id":"2305.11626","title":"CCT-Code: Cross-Consistency Training for Multilingual Clone Detection and Code Search","created":"2023-05-19","cited_by_count":4,"area":null},{"id":"2311.08157","title":"TransformCode: A Contrastive Learning Framework for Code Embedding via Subtree Transformation","created":"2023-11-10","cited_by_count":4,"area":null},{"id":"2503.14281","title":"XOXO: Stealthy Cross-Origin Context Poisoning Attacks against AI Coding Assistants","created":"2025-03-18","cited_by_count":4,"area":null},{"id":"2508.19558","title":"Functional Consistency of LLM Code Embeddings: A Self-Evolving Data Synthesis Framework for Benchmarking","created":"2025-08-27","cited_by_count":4,"area":"synth"},{"id":"2103.13020","title":"deGraphCS: Embedding Variable-based Flow Graph for Neural Code Search","created":"2021-03-24","cited_by_count":3,"area":null},{"id":"2111.08874","title":"GN-Transformer: Fusing Sequence and Graph Representation for Improved Code Summarization","created":"2021-11-17","cited_by_count":3,"area":null},{"id":"2202.04847","title":"A Survey on Artificial Intelligence for Source Code: A Dialogue Systems Perspective","created":"2022-02-10","cited_by_count":3,"area":null},{"id":"2206.08726","title":"Evaluation of Contrastive Learning with Various Code Representations for Code Clone Detection","created":"2022-06-17","cited_by_count":3,"area":null},{"id":"2305.05896","title":"A Black-Box Attack on Code Models via Representation Nearest Neighbor Search","created":"2023-05-10","cited_by_count":3,"area":null},{"id":"2308.13754","title":"ZC3: Zero-Shot Cross-Language Code Clone Detection","created":"2023-08-26","cited_by_count":3,"area":null},{"id":"2312.00918","title":"PACE: A Program Analysis Framework for Continuous Performance Prediction","created":"2023-12-01","cited_by_count":3,"area":null},{"id":"2408.04430","title":"The Struggles of LLMs in Cross-lingual Code Clone Detection","created":"2024-08-08","cited_by_count":3,"area":null},{"id":"2502.11167","title":"SURGE: On the Potential of Large Language Models as General-Purpose Surrogate Code Executors","created":"2025-02-16","cited_by_count":3,"area":null},{"id":"2505.01475","title":"CodeSSM: Towards State Space Models for Code Understanding","created":"2025-05-02","cited_by_count":3,"area":null},{"id":"2210.03123","title":"On the Effectiveness of Hybrid Pooling in Mixup-Based Graph Learning for Language Processing","created":"2022-10-06","cited_by_count":2,"area":null},{"id":"2211.11711","title":"CLAWSAT: Towards Both Robust and Accurate Code Models","created":"2022-11-21","cited_by_count":2,"area":null},{"id":"2311.07277","title":"AdaCCD: Adaptive Semantic Contrasts Discovery Based Cross Lingual Adaptation for Code Clone Detection","created":"2023-11-13","cited_by_count":2,"area":null},{"id":"2402.15769","title":"GenCode: A Generic Data Augmentation Framework for Boosting Deep Learning-Based Code Understanding","created":"2024-02-24","cited_by_count":2,"area":null},{"id":"2508.15423","title":"An Empirical Study of Knowledge Distillation for Code Understanding Tasks","created":"2025-08-21","cited_by_count":2,"area":"distill"},{"id":"2201.07984","title":"AstBERT: Enabling Language Model for Financial Code Understanding with Abstract Syntax Trees","created":"2022-01-20","cited_by_count":1,"area":null},{"id":"2208.08067","title":"K-ASTRO: Structure-Aware Adaptation of LLMs for Code Vulnerability Detection","created":"2022-08-17","cited_by_count":1,"area":null},{"id":"2303.07826","title":"Implant Global and Local Hierarchy Information to Sequence based Code Representation Models","created":"2023-03-14","cited_by_count":1,"area":null},{"id":"2402.16790","title":"Beyond Self-learned Attention: Mitigating Attention Bias in Transformer-based Models Using Attention Guidance","created":"2024-02-26","cited_by_count":1,"area":null},{"id":"2407.14530","title":"FuncEvalGMN: Evaluating Functional Correctness of SQL via Graph Matching Network","created":"2024-07-09","cited_by_count":1,"area":null},{"id":"2506.14470","title":"AST-Enhanced or AST-Overloaded? The Surprising Impact of Hybrid Graph Representations on Code Clone Detection","created":"2025-06-17","cited_by_count":1,"area":null},{"id":"2511.05476","title":"A Metamorphic Testing Perspective on Knowledge Distillation for Language Models of Code: Does the Student Deeply Mimic the Teacher?","created":"2025-11-07","cited_by_count":1,"area":"distill"},{"id":"2512.09385","title":"BugSweeper: Function-Level Detection of Smart Contract Vulnerabilities Using Graph Neural Networks","created":"2025-12-10","cited_by_count":1,"area":null},{"id":"2112.01259","title":"Borrowing from Similar Code: A Deep Learning NLP-Based Approach for Log Statement Automation","created":"2021-12-02","cited_by_count":0,"area":null},{"id":"2205.13730","title":"Understanding Long Programming Languages with Structure-Aware Sparse Attention","created":"2022-05-27","cited_by_count":0,"area":null},{"id":"2311.14850","title":"TrojanedCM: A Repository of Trojaned Large Language Models of Code","created":"2023-11-24","cited_by_count":0,"area":"safety"},{"id":"2312.16488","title":"Source Code is a Graph, Not a Sequence: A Cross-Lingual Perspective on Code Clone Detection","created":"2023-12-27","cited_by_count":0,"area":null},{"id":"2401.17967","title":"CONCORD: Towards a DSL for Configurable Graph Code Representation","created":"2024-01-31","cited_by_count":0,"area":null},{"id":"2405.02095","title":"Advanced Detection of Source Code Clones via an Ensemble of Unsupervised Similarity Measures","created":"2024-05-03","cited_by_count":0,"area":null},{"id":"2409.14644","title":"LSem2Vec: A Simple yet Effective Two-Stage Approach for Source Code Embedding","created":"2024-09-23","cited_by_count":0,"area":null},{"id":"2410.18107","title":"In-Context Code-Text Learning for Bimodal Software Engineering","created":"2024-10-08","cited_by_count":0,"area":null},{"id":"2502.13459","title":"Poisoned Source Code Detection in Code Models","created":"2025-02-19","cited_by_count":0,"area":null},{"id":"2510.15480","title":"Selecting and Combining Large Language Models for Scalable Code Clone Detection","created":"2025-10-17","cited_by_count":0,"area":null},{"id":"2510.17591","title":"HGAdapter: Hypergraph-based Adapters in Language Models for Code Summarization and Clone Detection","created":"2025-10-20","cited_by_count":0,"area":null},{"id":"2510.24241","title":"MAGNET: A Multi-Graph Attentional Network for Code Clone Detection","created":"2025-10-28","cited_by_count":0,"area":null},{"id":"2605.17458","title":"ClaHF: A Human Feedback-inspired Reinforcement Learning Framework for Improving Classification Tasks","created":"2026-05-17","cited_by_count":0,"area":"alignment"},{"id":"2608.04215","title":"A Unified Model for Cross-Domain Clone Detection via Model Merging","created":"2026-08-04","cited_by_count":0,"area":null}],"next_cursor":null}