{"total":46,"rows":[{"id":"2404.05427","title":"AutoCodeRover: Autonomous Program Improvement","created":"2024-04-08","cited_by_count":278,"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":"2406.01304","title":"CodeR: Issue Resolving with Multi-Agent and Task Graphs","created":"2024-06-03","cited_by_count":91,"area":null},{"id":"2404.14646","title":"Exploring and Unleashing the Power of Large Language Models in Automated Code Translation","created":"2024-04-23","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":"2303.03004","title":"xCodeEval: A Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and Retrieval","created":"2023-03-06","cited_by_count":47,"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":"2301.13246","title":"Conversational Automated Program Repair","created":"2023-01-30","cited_by_count":41,"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":"2408.12159","title":"Search-Based LLMs for Code Optimization","created":"2024-08-22","cited_by_count":28,"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":"2304.12015","title":"ITER: Iterative Neural Repair for Multi-Location Patches","created":"2023-04-24","cited_by_count":16,"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":"2207.11765","title":"Neurosymbolic Repair for Low-Code Formula Languages","created":"2022-07-24","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":"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":"2204.06643","title":"Fix Bugs with Transformer through a Neural-Symbolic Edit Grammar","created":"2022-04-13","cited_by_count":8,"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":"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":"2505.02931","title":"The Art of Repair: Optimizing Iterative Program Repair with Instruction-Tuned Models","created":"2025-05-05","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":"2309.12938","title":"Frustrated with Code Quality Issues? LLMs can Help!","created":"2023-09-22","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":"2404.04839","title":"AI for DevSecOps: A Landscape and Future Opportunities","created":"2024-04-07","cited_by_count":4,"area":null},{"id":"2411.15442","title":"Automatic High-quality Verilog Assertion Generation through Subtask-Focused Fine-Tuned LLMs and Iterative Prompting","created":"2024-11-23","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":"2210.02853","title":"NeuDep: Neural Binary Memory Dependence Analysis","created":"2022-10-04","cited_by_count":3,"area":null},{"id":"2503.09217","title":"Evaluating the Generalizability of LLMs in Automated Program Repair","created":"2025-03-12","cited_by_count":3,"area":null},{"id":"2507.19909","title":"The Impact of Fine-tuning Large Language Models on Automated Program Repair","created":"2025-07-26","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":"2408.08619","title":"PatUntrack: Automated Generating Patch Examples for Issue Reports without Tracked Insecure Code","created":"2024-08-16","cited_by_count":1,"area":null},{"id":"2506.13182","title":"From Empirical Evaluation to Context-Aware Enhancement: Repairing Regression Errors with LLMs","created":"2025-06-16","cited_by_count":1,"area":null},{"id":"2212.12584","title":"Neural Transition-based Parsing of Library Deprecations","created":"2022-12-23","cited_by_count":0,"area":null},{"id":"2404.01754","title":"Peer-aided Repairer: Empowering Large Language Models to Repair Advanced Student Assignments","created":"2024-04-02","cited_by_count":0,"area":null},{"id":"2406.16526","title":"NARRepair: Non-Autoregressive Code Generation Model for Automatic Program Repair","created":"2024-06-24","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":"2601.23059","title":"On the Impact of Code Comments for Automated Bug-Fixing: An Empirical Study","created":"2026-01-30","cited_by_count":0,"area":null},{"id":"2604.12994","title":"LogicEval: A Systematic Framework for Evaluating Automated Repair Techniques for Logical Vulnerabilities in Real-World Software","created":"2026-04-14","cited_by_count":0,"area":null},{"id":"2608.04611","title":"The Order Is the Guarantee: Verifier-Budgeted Code Deletion with Static-First Learned Proposals","created":"2026-08-05","cited_by_count":0,"area":null}],"next_cursor":null}