Papers Bug fixing
“Bug fixing” 태그가 달린 논문 62편 · 필터 해제
CoreCodeBench: A Configurable Multi-Scenario Repository-Level Benchmark
As Large Language Models (LLMs) demonstrate increasingly sophisticated code processing capabilities, evaluating their performance on engineering-level code remains challenging. Existing repository-level benchmarks primar…
Bug fixingCode Generationtest driven developmentThe Foundation Cracks: A Comprehensive Study on Bugs and Testing Practices in LLM Libraries
Large Language Model (LLM) libraries have emerged as the foundational infrastructure powering today's AI revolution, serving as the backbone for LLM deployment, inference optimization, fine-tuning, and production serving…
Bug fixingInference OptimizationLarge Language ModelSWE-Dev: Evaluating and Training Autonomous Feature-Driven Software Development
Large Language Models (LLMs) have shown strong capability in diverse software engineering tasks, e.g. code completion, bug fixing, and document generation. However, feature-driven development (FDD), a highly prevalent re…
Bug fixingChatbotCode CompletionReinforcement Learning (RL)LongCodeBench: Evaluating Coding LLMs at 1M Context Windows
Context lengths for models have grown rapidly, from thousands to millions of tokens in just a few years. The extreme context sizes of modern long-context models have made it difficult to construct realistic long-context …
Bug fixingVeriDebug: A Unified LLM for Verilog Debugging via Contrastive Embedding and Guided Correction
Large Language Models (LLMs) have demonstrated remarkable potential in debugging for various programming languages. However, the application of LLMs to Verilog debugging remains insufficiently explored. Here, we present …
Bug fixingAPE-Bench I: Towards File-level Automated Proof Engineering of Formal Math Libraries
Recent progress in large language models (LLMs) has shown promise in formal theorem proving, yet existing benchmarks remain limited to isolated, static proof tasks, failing to capture the iterative, engineering-intensive…
Automated Theorem ProvingBug fixingMathOn Simulation-Guided LLM-based Code Generation for Safe Autonomous Driving Software
Automated Driving System (ADS) is a safety-critical software system responsible for the interpretation of the vehicle's environment and making decisions accordingly. The unbounded complexity of the driving context, inclu…
Autonomous DrivingBug fixingCode GenerationCollision AvoidanceLess is More: Adaptive Program Repair with Bug Localization and Preference Learning
Automated Program Repair (APR) is a task to automatically generate patches for the buggy code. However, most research focuses on generating correct patches while ignoring the consistency between the fixed code and the or…
Bug fixingProgram RepairBenchmarking AI Models in Software Engineering: A Review, Search Tool, and Enhancement Protocol
Benchmarks are essential for consistent evaluation and reproducibility. The integration of Artificial Intelligence into Software Engineering (AI4SE) has given rise to numerous benchmarks for tasks such as code generation…
BenchmarkingBug fixingCode GenerationHumanEvalEmpirical evaluation of LLMs in predicting fixes of Configuration bugs in Smart Home System
This empirical study evaluates the effectiveness of Large Language Models (LLMs) in predicting fixes for configuration bugs in smart home systems. The research analyzes three prominent LLMs - GPT-4, GPT-4o (GPT-4 Turbo),…
Bug fixingRepository-level Code Search with Neural Retrieval Methods
This paper presents a multi-stage reranking system for repository-level code search, which leverages the vastly available commit histories of large open-source repositories to aid in bug fixing. We define the task of rep…
Bug fixingCode SearchRerankingRetrievalGREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code Generation
Large Language Models (LLMs) are becoming integral to daily life, showcasing their vast potential across various Natural Language Processing (NLP) tasks. Beyond NLP, LLMs are increasingly used in software development tas…
Bug fixingCode CompletionCode GenerationCode Translation+1CoRNStack: High-Quality Contrastive Data for Better Code Retrieval and Reranking
Effective code retrieval plays a crucial role in advancing code generation, bug fixing, and software maintenance, particularly as software systems increase in complexity. While current code embedding models have demonstr…
Bug fixingCode GenerationRerankingRetrieval+1An Empirical Study on LLM-based Agents for Automated Bug Fixing
Large language models (LLMs) and LLM-based Agents have been applied to fix bugs automatically, demonstrating the capability in addressing software defects by engaging in development environment interaction, iterative val…
Bug fixingFault localizationA Comprehensive Survey of AI-Driven Advancements and Techniques in Automated Program Repair and Code Generation
Bug fixing and code generation have been core research topics in software development for many years. The recent explosive growth in Large Language Models has completely transformed these spaces, putting in reach incredi…
Bug fixingCode GenerationProgram RepairSurveyPDC & DM-SFT: A Road for LLM SQL Bug-Fix Enhancing
Code Large Language Models (Code LLMs), such as Code llama and DeepSeek-Coder, have demonstrated exceptional performance in the code generation tasks. However, most existing models focus on the abilities of generating co…
Bug fixingCode GenerationMetRex: A Benchmark for Verilog Code Metric Reasoning Using LLMs
Large Language Models (LLMs) have been applied to various hardware design tasks, including Verilog code generation, EDA tool scripting, and RTL bug fixing. Despite this extensive exploration, LLMs are yet to be used for …
Bug fixingCode GenerationCharacterising Open Source Co-opetition in Company-hosted Open Source Software Projects: The Cases of PyTorch, TensorFlow, and Transformers
Companies, including market rivals, have long collaborated on the development of open source software (OSS), resulting in a tangle of co-operation and competition known as "open source co-opetition". While prior work inv…
Bug fixingDebug Smarter, Not Harder: AI Agents for Error Resolution in Computational Notebooks
Computational notebooks became indispensable tools for research-related development, offering unprecedented interactivity and flexibility in the development process. However, these benefits come at the cost of reproducib…
AI AgentBug fixingFrom Code to Correctness: Closing the Last Mile of Code Generation with Hierarchical Debugging
While large language models have made significant strides in code generation, the pass rate of the generated code is bottlenecked on subtle errors, often requiring human intervention to pass tests, especially for complex…
Auto DebuggingBug fixingCode GenerationHumanEval+1