Papers Program Repair
“Program Repair” 태그가 달린 논문 192편 · 필터 해제
If It's Not Buggy, Don't Fix It: On the Dynamics of Iterative Bug-fixing with LLMs
Large language models (LLMs) have become ubiquitous in software development, with LLM-based automated program repair tools increasingly used during code review. In this report, we explore the iterative blind use of LLMs …
Program RepairBetter Understanding, Better Fixes? A Study of Hallucination in LLM-based Automated Program Repair
Large language models (LLMs) have significantly advanced automated program repair (APR), yet existing evaluations remain largely result-centric and provide limited insight into hallucination during repair. In APR, halluc…
Program RepairRethinking Automated Program Repair: The Impact of Bug Complexity, Fault Localization, and LLM Cost-efficiency
Background: Software bugs remain a critical challenge in development, necessitating effective Automated Program Repair (APR) techniques. While Large Language Model (LLM)-based APR systems have shown promise, prior studie…
Program RepairHow Do LLMs Read Bug Reports? An Empirical Study of Attention in LLMs for Automated Program Repair
Large Language Model (LLM)-based Automated Program Repair systems are advancing rapidly, yet their performance remains inconsistent. Even when provided with the same contextual information, an LLM may generate a correct …
Program RepairBeyond Fail-to-Pass: Iterative Hardening of Co-Generated Bug Reproduction Tests and Fixes
Large language models (LLMs) have made automated program repair (APR) increasingly practical for real-world bugs, but repairing directly from bug reports remains underconstrained. Bug reproduction tests (BRTs) help close…
Program RepairMulti-Perspective Agentic Program Repair via Code Property Graphs and Temporal Execution Graphs
Large language models (LLMs) have improved automated program repair (APR), but two limitations remain. First, raw execution traces are often too large and repetitive to serve as effective model context. Second, repeated …
Program RepairTeaching Code LLMs to Reason with Intermediate Formal Specifications
Unlike natural-language specifications, executable formal specifications provide machine-checkable constraints for verifying, debugging, and repairing code. However, writing such specifications is labor-intensive, and ex…
Program RepairContextSniper: AntTrail's Token-Efficient Code Memory for Repository-Level Program Repair
Large language model agents can repair real repository issues, but they often spend large context budgets on whole-file reads, broad searches, and long terminal outputs where useful evidence is mixed with irrelevant code…
Program RepairTraceView: Interactive Visualization of Agentic Program Repair Trajectories
LLM-based automated program repair (APR) agents generate patches to fix software bugs with minimal human intervention. These agents often produce long trajectories of reasoning, tool use, and feedback to produce candidat…
Program RepairHDSL: A Hierarchical Domain-Specific Language for Structured 3D Indoor Scene Generation and Localized Editing with LLM Agents
Text-driven indoor scene generation and editing require an intermediate representation that language models can both produce and revise. Existing LLM-based systems often rely on scene graphs or global constraint lists, w…
Scene GenerationProgram RepairSWE-Mutation: Can LLMs Generate Reliable Test Suites in Software Engineering?
Evaluating software engineering capabilities has become a core component of modern large language models (LLMs); however, the key bottleneck hindering further scaling lies not in the scarcity of high-quality solutions, b…
Reinforcement LearningProgram RepairBLAgent: Agentic RAG for File-Level Bug Localization
Bug localization remains a key bottleneck for large language model (LLM)-based software maintenance, where accurately identifying faulty code is essential for debugging, root cause analysis, triage, and automated program…
Program RepairBoostAPR: Boosting Automated Program Repair via Execution-Grounded Reinforcement Learning with Dual Reward Models
Reinforcement learning for program repair is hindered by sparse execution feedback and coarse sequence-level rewards that obscure which edits actually fix bugs. We present BoostAPR, a three-stage framework addressing the…
Reinforcement LearningProgram RepairARISE: A Repository-level Graph Representation and Toolset for Agentic Program Repair and Fault Localization
Automated program repair at repository scale requires an agent to locate a fault among thousands of files and synthesize a correct patch. Existing graph-based agents represent how a repository is organized into files, cl…
Program RepairA Metamorphic Testing Approach to Diagnosing Memorization in LLM-Based Program Repair
LLM-based automated program repair (APR) techniques have shown promising results in reducing debugging costs. However, prior results can be affected by data leakage: large language models (LLMs) may memorize bug fixes wh…
Program RepairClover: A Neural-Symbolic Agentic Harness with Stochastic Tree-of-Thoughts for Verified RTL Repair
RTL program repair remains a critical bottleneck in hardware design and verification. Traditional automatic program repair (APR) methods rely on predefined templates and synthesis, limiting their bug coverage. Large lang…
Program RepairProject Prometheus: Bridging the Intent Gap in Agentic Program Repair via Reverse-Engineered Executable Specifications
The transition from neural machine translation to agentic workflows has revolutionized Automated Program Repair (APR). However, existing agents, despite their advanced reasoning capabilities, frequently suffer from the `…
Machine TranslationCode GenerationProgram RepairLogicEval: A Systematic Framework for Evaluating Automated Repair Techniques for Logical Vulnerabilities in Real-World Software
Logical vulnerabilities in software stem from flaws in program logic rather than memory safety, which can lead to critical security failures. Although existing automated program repair techniques primarily focus on repai…
Program RepairPatchRecall: Patch-Driven Retrieval for Automated Program Repair
Retrieving the correct set of files from a large codebase is a crucial step in Automated Program Repair (APR). High recall is necessary to ensure that the relevant files are included, but simply increasing the number of …
Program RepairFine-grained Approaches for Confidence Calibration of LLMs in Automated Code Revision
In today's AI-assisted software engineering landscape, developers increasingly depend on LLMs that are highly capable, yet inherently imperfect. The tendency of these models to produce incorrect outputs can reduce develo…
Program Repair