{"total":231,"rows":[{"id":"2108.07732","title":"Program Synthesis with Large Language Models","created":"2021-08-16","cited_by_count":3637,"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":"2103.06333","title":"Unified Pre-training for Program Understanding and Generation","created":"2021-03-10","cited_by_count":366,"area":null},{"id":"2302.05527","title":"CodeBERTScore: Evaluating Code Generation with Pretrained Models of Code","created":"2023-02-10","cited_by_count":102,"area":null},{"id":"2103.00073","title":"CURE: Code-Aware Neural Machine Translation for Automatic Program Repair","created":"2021-02-26","cited_by_count":86,"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":"2103.11318","title":"Language-Agnostic Representation Learning of Source Code from Structure and Context","created":"2021-03-21","cited_by_count":63,"area":null},{"id":"2108.04556","title":"SynCoBERT: Syntax-Guided Multi-Modal Contrastive Pre-Training for Code Representation","created":"2021-08-10","cited_by_count":63,"area":null},{"id":"2202.06840","title":"What Do They Capture? -- A Structural Analysis of Pre-Trained Language Models for Source Code","created":"2022-02-14","cited_by_count":50,"area":null},{"id":"2207.11280","title":"PanGu-Coder: Program Synthesis with Function-Level Language Modeling","created":"2022-07-22","cited_by_count":48,"area":null},{"id":"2212.08109","title":"An Empirical Study of Deep Learning Models for Vulnerability Detection","created":"2022-12-15","cited_by_count":46,"area":null},{"id":"2112.02043","title":"Multilingual training for Software Engineering","created":"2021-12-03","cited_by_count":43,"area":null},{"id":"2105.12485","title":"TreeBERT: A Tree-Based Pre-Trained Model for Programming Language","created":"2021-05-26","cited_by_count":41,"area":null},{"id":"2101.04808","title":"MLGO: a Machine Learning Guided Compiler Optimizations Framework","created":"2021-01-13","cited_by_count":40,"area":null},{"id":"2206.01335","title":"Code Generation Tools (Almost) for Free? A Study of Few-Shot, Pre-Trained Language Models on Code","created":"2022-06-02","cited_by_count":39,"area":null},{"id":"2108.11308","title":"What do pre-trained code models know about code?","created":"2021-08-25","cited_by_count":36,"area":null},{"id":"2401.03315","title":"Malla: Demystifying Real-world Large Language Model Integrated Malicious Services","created":"2024-01-06","cited_by_count":33,"area":null},{"id":"2307.02503","title":"Natural Language Generation and Understanding of Big Code for AI-Assisted Programming: A Review","created":"2023-07-04","cited_by_count":32,"area":null},{"id":"2412.12591","title":"LLMs are Also Effective Embedding Models: An In-depth Overview","created":"2024-12-17","cited_by_count":32,"area":null},{"id":"2308.14731","title":"Distilled GPT for Source Code Summarization","created":"2023-08-28","cited_by_count":31,"area":null},{"id":"2103.09499","title":"Code Completion by Modeling Flattened Abstract Syntax Trees as Graphs","created":"2021-03-17","cited_by_count":30,"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":"2111.05711","title":"Counterfactual Explanations for Models of Code","created":"2021-11-10","cited_by_count":25,"area":null},{"id":"2103.11599","title":"Project-Level Encoding for Neural Source Code Summarization of Subroutines","created":"2021-03-22","cited_by_count":24,"area":null},{"id":"2110.09610","title":"A Survey on Machine Learning Techniques for Source Code Analysis","created":"2021-10-18","cited_by_count":24,"area":null},{"id":"2302.08624","title":"InstructABSA: Instruction Learning for Aspect Based Sentiment Analysis","created":"2023-02-16","cited_by_count":23,"area":null},{"id":"2106.03353","title":"Understanding Neural Code Intelligence Through Program Simplification","created":"2021-06-07","cited_by_count":22,"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":"2103.07845","title":"Improving Code Summarization with Block-wise Abstract Syntax Tree Splitting","created":"2021-03-14","cited_by_count":19,"area":null},{"id":"2106.08704","title":"Memorization and Generalization in Neural Code Intelligence Models","created":"2021-06-16","cited_by_count":19,"area":null},{"id":"2309.04142","title":"Trustworthy and Synergistic Artificial Intelligence for Software Engineering: Vision and Roadmaps","created":"2023-09-08","cited_by_count":19,"area":null},{"id":"2404.08018","title":"Analyzing the Performance of Large Language Models on Code Summarization","created":"2024-04-10","cited_by_count":19,"area":null},{"id":"2201.08441","title":"VUDENC: Vulnerability Detection with Deep Learning on a Natural Codebase for Python","created":"2022-01-20","cited_by_count":17,"area":null},{"id":"2304.12512","title":"Semantic Compression With Large Language Models","created":"2023-04-25","cited_by_count":17,"area":null},{"id":"2206.03545","title":"Code-DKT: A Code-based Knowledge Tracing Model for Programming Tasks","created":"2022-06-07","cited_by_count":16,"area":null},{"id":"2305.14129","title":"GrACE: Generation using Associated Code Edits","created":"2023-05-23","cited_by_count":16,"area":null},{"id":"2111.02671","title":"GraphSearchNet: Enhancing GNNs via Capturing Global Dependencies for Semantic Code Search","created":"2021-11-04","cited_by_count":15,"area":null},{"id":"2202.06043","title":"RoPGen: Towards Robust Code Authorship Attribution via Automatic Coding Style Transformation","created":"2022-02-12","cited_by_count":15,"area":null},{"id":"2204.10049","title":"On Distribution Shift in Learning-based Bug Detectors","created":"2022-04-21","cited_by_count":15,"area":null},{"id":"2205.02029","title":"CODE-MVP: Learning to Represent Source Code from Multiple Views with Contrastive Pre-Training","created":"2022-05-04","cited_by_count":15,"area":null},{"id":"2205.06439","title":"AEON: A Method for Automatic Evaluation of NLP Test Cases","created":"2022-05-13","cited_by_count":15,"area":null},{"id":"2309.01940","title":"CodeApex: A Bilingual Programming Evaluation Benchmark for Large Language Models","created":"2023-09-05","cited_by_count":15,"area":null},{"id":"2305.05779","title":"Learning to Parallelize with OpenMP by Augmented Heterogeneous AST Representation","created":"2023-05-09","cited_by_count":14,"area":null},{"id":"2306.16171","title":"A systematic literature review on source code similarity measurement and clone detection: techniques, applications, and challenges","created":"2023-06-28","cited_by_count":14,"area":null},{"id":"2309.09826","title":"Efficient Avoidance of Vulnerabilities in Auto-completed Smart Contract Code Using Vulnerability-constrained Decoding","created":"2023-09-18","cited_by_count":14,"area":null},{"id":"2111.01633","title":"Neural Program Generation Modulo Static Analysis","created":"2021-10-26","cited_by_count":13,"area":null},{"id":"2201.07420","title":"Cross-Language Binary-Source Code Matching with Intermediate Representations","created":"2022-01-19","cited_by_count":13,"area":null},{"id":"2212.10079","title":"A Survey on Pretrained Language Models for Neural Code Intelligence","created":"2022-12-20","cited_by_count":13,"area":null},{"id":"2205.10364","title":"Learning to Reverse DNNs from AI Programs Automatically","created":"2022-05-20","cited_by_count":12,"area":null},{"id":"2107.00992","title":"Multimodal Representation for Neural Code Search","created":"2021-07-02","cited_by_count":11,"area":null},{"id":"2108.08041","title":"DeepCVA: Automated Commit-level Vulnerability Assessment with Deep Multi-task Learning","created":"2021-08-18","cited_by_count":11,"area":null},{"id":"2108.09619","title":"Impact of Evaluation Methodologies on Code Summarization","created":"2021-08-22","cited_by_count":11,"area":null},{"id":"2109.04738","title":"On the validity of pre-trained transformers for natural language processing in the software engineering domain","created":"2021-09-10","cited_by_count":11,"area":null},{"id":"2109.08079","title":"Context-NER : Contextual Phrase Generation at Scale","created":"2021-09-16","cited_by_count":11,"area":null},{"id":"2306.17281","title":"HPC-Coder: Modeling Parallel Programs using Large Language Models","created":"2023-06-29","cited_by_count":11,"area":null},{"id":"2402.15100","title":"Studying LLM Performance on Closed- and Open-source Data","created":"2024-02-23","cited_by_count":11,"area":null},{"id":"2405.16792","title":"Laurel: Unblocking Automated Verification with Large Language Models","created":"2024-05-27","cited_by_count":11,"area":null},{"id":"2106.01367","title":"On using distributed representations of source code for the detection of C security vulnerabilities","created":"2021-06-01","cited_by_count":10,"area":null},{"id":"2308.03312","title":"Exploiting Code Symmetries for Learning Program Semantics","created":"2023-08-07","cited_by_count":10,"area":null},{"id":"2308.12659","title":"kTrans: Knowledge-Aware Transformer for Binary Code Embedding","created":"2023-08-24","cited_by_count":10,"area":null},{"id":"2406.09606","title":"Cross-Modality Program Representation Learning for Electronic Design Automation with High-Level Synthesis","created":"2024-06-13","cited_by_count":10,"area":null},{"id":"2112.08273","title":"Programming Knowledge Tracing: A Comprehensive Dataset and A New Model","created":"2021-12-11","cited_by_count":9,"area":null},{"id":"2205.15479","title":"HierarchyNet: Learning to Summarize Source Code with Heterogeneous Representations","created":"2022-05-31","cited_by_count":9,"area":null},{"id":"2207.08389","title":"MLGOPerf: An ML Guided Inliner to Optimize Performance","created":"2022-07-18","cited_by_count":9,"area":null},{"id":"2209.03465","title":"TAG: Learning Circuit Spatial Embedding From Layouts","created":"2022-09-07","cited_by_count":9,"area":null},{"id":"2210.03894","title":"GRANITE: A Graph Neural Network Model for Basic Block Throughput Estimation","created":"2022-10-08","cited_by_count":9,"area":null},{"id":"2410.24119","title":"Leveraging Large Language Models for Code Translation and Software Development in Scientific Computing","created":"2024-10-31","cited_by_count":9,"area":null},{"id":"2506.17323","title":"I Know Which LLM Wrote Your Code Last Summer: LLM generated Code Stylometry for Authorship Attribution","created":"2025-06-18","cited_by_count":9,"area":null},{"id":"2103.04448","title":"Toward Semi-Automatic Misconception Discovery Using Code Embeddings","created":"2021-03-07","cited_by_count":8,"area":null},{"id":"2105.03317","title":"Toward Code Generation: A Survey and Lessons from Semantic Parsing","created":"2021-04-26","cited_by_count":8,"area":null},{"id":"2401.15545","title":"PPM: Automated Generation of Diverse Programming Problems for Benchmarking Code Generation Models","created":"2024-01-28","cited_by_count":8,"area":null},{"id":"2205.14374","title":"Syntax-Guided Program Reduction for Understanding Neural Code Intelligence Models","created":"2022-05-28","cited_by_count":7,"area":null},{"id":"2208.04125","title":"Is this Change the Answer to that Problem? Correlating Descriptions of Bug and Code Changes for Evaluating Patch Correctness","created":"2022-08-08","cited_by_count":7,"area":null},{"id":"2210.09597","title":"Soft-Labeled Contrastive Pre-training for Function-level Code Representation","created":"2022-10-18","cited_by_count":7,"area":null},{"id":"2302.02343","title":"LExecutor: Learning-Guided Execution","created":"2023-02-05","cited_by_count":7,"area":null},{"id":"2410.10829","title":"Test Case-Informed Knowledge Tracing for Open-ended Coding Tasks","created":"2024-09-28","cited_by_count":7,"area":null},{"id":"2509.25257","title":"RANGER -- Repository-Level Agent for Graph-Enhanced Retrieval","created":"2025-09-27","cited_by_count":7,"area":null},{"id":"2108.10168","title":"CGEMs: A Metric Model for Automatic Code Generation using GPT-3","created":"2021-08-23","cited_by_count":6,"area":null},{"id":"2202.12481","title":"Multi-View Graph Representation for Programming Language Processing: An Investigation into Algorithm Detection","created":"2022-02-25","cited_by_count":6,"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":"2206.05480","title":"CodeS: Towards Code Model Generalization Under Distribution Shift","created":"2022-06-11","cited_by_count":6,"area":null},{"id":"2206.06460","title":"MetaTPTrans: A Meta Learning Approach for Multilingual Code Representation Learning","created":"2022-06-13","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":"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":"2405.15512","title":"ChatGPT Code Detection: Techniques for Uncovering the Source of Code","created":"2024-05-24","cited_by_count":6,"area":null},{"id":"2101.03999","title":"A Neural Question Answering System for Basic Questions about Subroutines","created":"2021-01-11","cited_by_count":5,"area":null},{"id":"2103.11448","title":"Exploiting Method Names to Improve Code Summarization: A Deliberation Multi-Task Learning Approach","created":"2021-03-21","cited_by_count":5,"area":null},{"id":"2106.13928","title":"Toward Less Hidden Cost of Code Completion with Acceptance and Ranking Models","created":"2021-06-26","cited_by_count":5,"area":null},{"id":"2107.10989","title":"Estimating Predictive Uncertainty Under Program Data Distribution Shift","created":"2021-07-23","cited_by_count":5,"area":null},{"id":"2109.03341","title":"Software Vulnerability Detection via Deep Learning over Disaggregated Code Graph Representation","created":"2021-09-07","cited_by_count":5,"area":null},{"id":"2203.08417","title":"On the Use of Fine-grained Vulnerable Code Statements for Software Vulnerability Assessment Models","created":"2022-03-16","cited_by_count":5,"area":null},{"id":"2204.12916","title":"GypSum: Learning Hybrid Representations for Code Summarization","created":"2022-04-26","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":"2208.09614","title":"An ensemble meta-estimator to predict source code testability","created":"2022-08-20","cited_by_count":5,"area":null},{"id":"2304.11072","title":"An Unbiased Transformer Source Code Learning with Semantic Vulnerability Graph","created":"2023-04-17","cited_by_count":5,"area":null},{"id":"2306.06228","title":"AVScan2Vec: Feature Learning on Antivirus Scan Data for Production-Scale Malware Corpora","created":"2023-06-09","cited_by_count":5,"area":null},{"id":"2308.07429","title":"Semantic Similarity Loss for Neural Source Code Summarization","created":"2023-08-14","cited_by_count":5,"area":null},{"id":"2308.13507","title":"Large Language Models Should Ask Clarifying Questions to Increase Confidence in Generated Code","created":"2023-08-25","cited_by_count":5,"area":null},{"id":"2312.00413","title":"Abstract Syntax Tree for Programming Language Understanding and Representation: How Far Are We?","created":"2023-12-01","cited_by_count":5,"area":null}],"next_cursor":"100"}