{"total":121,"rows":[{"id":"2108.07732","title":"Program Synthesis with Large Language Models","created":"2021-08-16","cited_by_count":3637,"area":null},{"id":"2204.05999","title":"InCoder: A Generative Model for Code Infilling and Synthesis","created":"2022-04-12","cited_by_count":498,"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":"2406.01297","title":"When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMs","created":"2024-06-03","cited_by_count":240,"area":null},{"id":"2105.12655","title":"CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks","created":"2021-05-25","cited_by_count":200,"area":null},{"id":"2306.09896","title":"Is Self-Repair a Silver Bullet for Code Generation?","created":"2023-06-16","cited_by_count":197,"area":null},{"id":"2304.00385","title":"Keep the Conversation Going: Fixing 162 out of 337 bugs for $0.42 each using ChatGPT","created":"2023-04-01","cited_by_count":125,"area":null},{"id":"2303.04910","title":"Baldur: Whole-Proof Generation and Repair with Large Language Models","created":"2023-03-08","cited_by_count":119,"area":null},{"id":"2307.04349","title":"RLTF: Reinforcement Learning from Unit Test Feedback","created":"2023-07-10","cited_by_count":114,"area":null},{"id":"2112.02125","title":"Examining Zero-Shot Vulnerability Repair with Large Language Models","created":"2021-12-03","cited_by_count":105,"area":null},{"id":"2301.13816","title":"Execution-based Code Generation using Deep Reinforcement Learning","created":"2023-01-31","cited_by_count":99,"area":null},{"id":"2107.08760","title":"CVEfixes: Automated Collection of Vulnerabilities and Their Fixes from Open-Source Software","created":"2021-07-19","cited_by_count":91,"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":"2110.06773","title":"Leveraging Automated Unit Tests for Unsupervised Code Translation","created":"2021-10-13","cited_by_count":82,"area":null},{"id":"2106.06600","title":"Break-It-Fix-It: Unsupervised Learning for Program Repair","created":"2021-06-11","cited_by_count":71,"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":"2106.08253","title":"A Syntax-Guided Edit Decoder for Neural Program Repair","created":"2021-06-15","cited_by_count":57,"area":null},{"id":"2309.00608","title":"Copiloting the Copilots: Fusing Large Language Models with Completion Engines for Automated Program Repair","created":"2023-09-01","cited_by_count":55,"area":null},{"id":"2305.18607","title":"How Effective Are Neural Networks for Fixing Security Vulnerabilities","created":"2023-05-29","cited_by_count":52,"area":null},{"id":"2312.15698","title":"RepairLLaMA: Efficient Representations and Fine-Tuned Adapters for Program Repair","created":"2023-12-25","cited_by_count":50,"area":null},{"id":"2203.05132","title":"Compilable Neural Code Generation with Compiler Feedback","created":"2022-03-10","cited_by_count":47,"area":null},{"id":"2308.11148","title":"LLaMA-Reviewer: Advancing Code Review Automation with Large Language Models through Parameter-Efficient Fine-Tuning","created":"2023-08-22","cited_by_count":47,"area":null},{"id":"2303.11455","title":"Large Language Models and Simple, Stupid Bugs","created":"2023-03-20","cited_by_count":45,"area":null},{"id":"2305.14752","title":"A New Era in Software Security: Towards Self-Healing Software via Large Language Models and Formal Verification","created":"2023-05-24","cited_by_count":42,"area":null},{"id":"2408.05534","title":"Can LLMs Replace Manual Annotation of Software Engineering Artifacts?","created":"2024-08-10","cited_by_count":42,"area":null},{"id":"2104.08308","title":"Neural Transfer Learning for Repairing Security Vulnerabilities in C Code","created":"2021-04-16","cited_by_count":36,"area":null},{"id":"2405.02213","title":"Automatic Programming: Large Language Models and Beyond","created":"2024-05-03","cited_by_count":36,"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":"2306.03438","title":"Large Language Models of Code Fail at Completing Code with Potential Bugs","created":"2023-06-06","cited_by_count":29,"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":"2110.09610","title":"A Survey on Machine Learning Techniques for Source Code Analysis","created":"2021-10-18","cited_by_count":24,"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":"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":"2104.07896","title":"Generating Bug-Fixes Using Pretrained Transformers","created":"2021-04-16","cited_by_count":18,"area":null},{"id":"2105.09352","title":"DeepDebug: Fixing Python Bugs Using Stack Traces, Backtranslation, and Code Skeletons","created":"2021-05-19","cited_by_count":17,"area":null},{"id":"2101.12087","title":"Learning Structural Edits via Incremental Tree Transformations","created":"2021-01-28","cited_by_count":16,"area":null},{"id":"2304.12015","title":"ITER: Iterative Neural Repair for Multi-Location Patches","created":"2023-04-24","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":"2304.03816","title":"Towards Generating Functionally Correct Code Edits from Natural Language Issue Descriptions","created":"2023-04-07","cited_by_count":14,"area":null},{"id":"2308.12415","title":"Benchmarking Causal Study to Interpret Large Language Models for Source Code","created":"2023-08-23","cited_by_count":14,"area":null},{"id":"2302.01857","title":"KNOD: Domain Knowledge Distilled Tree Decoder for Automated Program Repair","created":"2023-02-03","cited_by_count":13,"area":null},{"id":"2302.03788","title":"Toward a Theory of Causation for Interpreting Neural Code Models","created":"2023-02-07","cited_by_count":12,"area":null},{"id":"2404.08877","title":"Aligning the Objective of LLM-based Program Repair","created":"2024-04-13","cited_by_count":12,"area":null},{"id":"2209.03230","title":"AutoPruner: Transformer-Based Call Graph Pruning","created":"2022-09-07","cited_by_count":11,"area":null},{"id":"2301.01113","title":"Invalidator: Automated Patch Correctness Assessment via Semantic and Syntactic Reasoning","created":"2023-01-03","cited_by_count":11,"area":null},{"id":"2304.01102","title":"RunBugRun -- An Executable Dataset for Automated Program Repair","created":"2023-04-03","cited_by_count":11,"area":null},{"id":"2407.08983","title":"Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanations","created":"2024-07-12","cited_by_count":11,"area":null},{"id":"2211.14875","title":"Detect-Localize-Repair: A Unified Framework for Learning to Debug with CodeT5","created":"2022-11-27","cited_by_count":10,"area":null},{"id":"2212.11414","title":"Improving Automated Program Repair with Domain Adaptation","created":"2022-12-21","cited_by_count":10,"area":null},{"id":"2304.07840","title":"Enhancing Automated Program Repair through Fine-tuning and Prompt Engineering","created":"2023-04-16","cited_by_count":10,"area":null},{"id":"2504.08734","title":"Towards an Understanding of Context Utilization in Code Intelligence","created":"2025-04-11","cited_by_count":10,"area":null},{"id":"2308.03873","title":"Evaluating and Explaining Large Language Models for Code Using Syntactic Structures","created":"2023-08-07","cited_by_count":9,"area":null},{"id":"2104.14671","title":"SYNFIX: Automatically Fixing Syntax Errors using Compiler Diagnostics","created":"2021-04-29","cited_by_count":8,"area":null},{"id":"2204.06643","title":"Fix Bugs with Transformer through a Neural-Symbolic Edit Grammar","created":"2022-04-13","cited_by_count":8,"area":null},{"id":"2302.04048","title":"Automating Code-Related Tasks Through Transformers: The Impact of Pre-training","created":"2023-02-08","cited_by_count":8,"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":"2302.02343","title":"LExecutor: Learning-Guided Execution","created":"2023-02-05","cited_by_count":7,"area":null},{"id":"2303.10494","title":"Revisiting the Plastic Surgery Hypothesis via Large Language Models","created":"2023-03-18","cited_by_count":7,"area":null},{"id":"2402.13291","title":"DeepCode AI Fix: Fixing Security Vulnerabilities with Large Language Models","created":"2024-02-19","cited_by_count":7,"area":null},{"id":"2405.05455","title":"Automated Program Repair: Emerging trends pose and expose problems for benchmarks","created":"2024-05-08","cited_by_count":7,"area":null},{"id":"2409.10033","title":"Can GPT-O1 Kill All Bugs? An Evaluation of GPT-Family LLMs on QuixBugs","created":"2024-09-16","cited_by_count":7,"area":null},{"id":"2312.05275","title":"Exploring the Limits of ChatGPT in Software Security Applications","created":"2023-12-08","cited_by_count":6,"area":null},{"id":"2601.13384","title":"From Completion to Editing: Unlocking Context-Aware Code Infilling via Search-and-Replace Instruction Tuning","created":"2026-01-19","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":"2210.04802","title":"SimSCOOD: Systematic Analysis of Out-of-Distribution Generalization in Fine-tuned Source Code Models","created":"2022-10-10","cited_by_count":5,"area":null},{"id":"2305.04940","title":"The EarlyBIRD Catches the Bug: On Exploiting Early Layers of Encoder Models for More Efficient Code Classification","created":"2023-05-08","cited_by_count":5,"area":null},{"id":"2312.04986","title":"Out of Context: How important is Local Context in Neural Program Repair?","created":"2023-12-08","cited_by_count":5,"area":null},{"id":"2402.11892","title":"Towards Reliable Evaluation of Neural Program Repair with Natural Robustness Testing","created":"2024-02-19","cited_by_count":5,"area":null},{"id":"2410.16655","title":"Memory-Efficient Large Language Models for Program Repair with Semantic-Guided Patch Generation","created":"2024-10-22","cited_by_count":5,"area":null},{"id":"2107.13296","title":"Predicting Patch Correctness Based on the Similarity of Failing Test Cases","created":"2021-07-28","cited_by_count":4,"area":null},{"id":"2205.01293","title":"A Survey of Deep Learning Models for Structural Code Understanding","created":"2022-05-03","cited_by_count":4,"area":null},{"id":"2207.00301","title":"Can we learn from developer mistakes? Learning to localize and repair real bugs from real bug fixes","created":"2022-07-01","cited_by_count":4,"area":null},{"id":"2210.03003","title":"MIXCODE: Enhancing Code Classification by Mixup-Based Data Augmentation","created":"2022-10-06","cited_by_count":4,"area":null},{"id":"2305.13884","title":"Multi-Granularity Detector for Vulnerability Fixes","created":"2023-05-23","cited_by_count":4,"area":null},{"id":"2310.16253","title":"ConDefects: A New Dataset to Address the Data Leakage Concern for LLM-based Fault Localization and Program Repair","created":"2023-10-25","cited_by_count":4,"area":null},{"id":"2404.04839","title":"AI for DevSecOps: A Landscape and Future Opportunities","created":"2024-04-07","cited_by_count":4,"area":null},{"id":"2412.03905","title":"Integrating Various Software Artifacts for Better LLM-based Bug Localization and Program Repair","created":"2024-12-05","cited_by_count":4,"area":null},{"id":"2505.17703","title":"Gradient-Based Program Repair: Fixing Bugs in Continuous Program Spaces","created":"2025-05-23","cited_by_count":4,"area":null},{"id":"2109.10476","title":"Self-Supervised Learning to Prove Equivalence Between Straight-Line Programs via Rewrite Rules","created":"2021-09-22","cited_by_count":3,"area":null},{"id":"2211.06335","title":"Using Developer Discussions to Guide Fixing Bugs in Software","created":"2022-11-11","cited_by_count":3,"area":null},{"id":"2401.07994","title":"Assessing the Latent Automated Program Repair Capabilities of Large Language Models using Round-Trip Translation","created":"2024-01-15","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":"2512.24635","title":"DynaFix: Iterative Automated Program Repair Driven by Execution-Level Dynamic Information","created":"2025-12-31","cited_by_count":3,"area":null},{"id":"2106.02452","title":"Proving Equivalence Between Complex Expressions Using Graph-to-Sequence Neural Models","created":"2021-06-01","cited_by_count":2,"area":null},{"id":"2109.12296","title":"Jointly Learning to Repair Code and Generate Commit Message","created":"2021-09-25","cited_by_count":2,"area":null},{"id":"2309.14760","title":"Program Repair with Minimal Edits Using CodeT5","created":"2023-09-26","cited_by_count":2,"area":null},{"id":"2501.12339","title":"Treefix: Enabling Execution with a Tree of Prefixes","created":"2025-01-21","cited_by_count":2,"area":null},{"id":"2502.12490","title":"UniGenCoder: Merging Seq2Seq and Seq2Tree Paradigms for Unified Code Generation","created":"2025-02-18","cited_by_count":2,"area":null},{"id":"2102.04625","title":"WheaCha: A Method for Explaining the Predictions of Models of Code","created":"2021-02-09","cited_by_count":1,"area":null},{"id":"2202.03055","title":"Enabling Automatic Repair of Source Code Vulnerabilities Using Data-Driven Methods","created":"2022-02-07","cited_by_count":1,"area":null},{"id":"2205.13342","title":"Leveraging Causal Inference for Explainable Automatic Program Repair","created":"2022-05-26","cited_by_count":1,"area":null},{"id":"2307.02443","title":"An Exploratory Literature Study on Sharing and Energy Use of Language Models for Source Code","created":"2023-07-05","cited_by_count":1,"area":null},{"id":"2308.08943","title":"Towards Automatically Addressing Self-Admitted Technical Debt: How Far Are We?","created":"2023-08-17","cited_by_count":1,"area":null},{"id":"2502.07046","title":"SnipGen: A Mining Repository Framework for Evaluating LLMs for Code","created":"2025-02-10","cited_by_count":1,"area":null},{"id":"2506.04987","title":"A Multi-Dataset Evaluation of Models for Automated Vulnerability Repair","created":"2025-06-05","cited_by_count":1,"area":null},{"id":"2506.18191","title":"Call Me Maybe: Enhancing JavaScript Call Graph Construction using Graph Neural Networks","created":"2025-06-22","cited_by_count":1,"area":null},{"id":"2103.08747","title":"Embedding Code Contexts for Cryptographic API Suggestion:New Methodologies and Comparisons","created":"2021-03-15","cited_by_count":0,"area":null},{"id":"2107.08262","title":"Tea: Program Repair Using Neural Network Based on Program Information Attention Matrix","created":"2021-07-17","cited_by_count":0,"area":null},{"id":"2111.00580","title":"Text Classification for Task-based Source Code Related Questions","created":"2021-10-31","cited_by_count":0,"area":null},{"id":"2205.08585","title":"CV4Code: Sourcecode Understanding via Visual Code Representations","created":"2022-05-11","cited_by_count":0,"area":null}],"next_cursor":"100"}