:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Rawal, Ruchit, Pădurean, Victor-Alexandru, Apel, Sven, Singla, Adish, Toneva, Mariya
Format:	Preprint
Published:	2024
Subjects:	Software Engineering
Online Access:	https://arxiv.org/abs/2412.12471
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

BugSpotter: Automated Generation of Code Debugging Exercises
by: Pădurean, Victor-Alexandru, et al.
Published: (2024)

How do Humans and LLMs Process Confusing Code?
by: Abdelsalam, Youssef, et al.
Published: (2025)

Pragmatic Reasoning improves LLM Code Generation
by: Cao, Zhuchen, et al.
Published: (2025)

Perturbed examples reveal invariances shared by language models
by: Rawal, Ruchit, et al.
Published: (2023)

Benchmarking Generative Models on Computational Thinking Tests in Elementary Visual Programming
by: Pădurean, Victor-Alexandru, et al.
Published: (2024)

Neural Task Synthesis for Visual Programming
by: Pădurean, Victor-Alexandru, et al.
Published: (2023)

An Annotation-based Approach for Finding Bugs in Neural Network Programs
by: Rezaalipour, Mohammad, et al.
Published: (2021)

Evidence Tetris in the Pixelated World of Validity Threats
by: Wyrich, Marvin, et al.
Published: (2024)

BugsInPy: A Database of Existing Bugs in Python Programs to Enable Controlled Testing and Debugging Studies
by: Widyasari, Ratnadira, et al.
Published: (2024)

Debug2Fix: Can Interactive Debugging Help Coding Agents Fix More Bugs?
by: Garg, Spandan, et al.
Published: (2026)

Agentic Property-Based Testing: Finding Bugs Across the Python Ecosystem
by: Maaz, Muhammad, et al.
Published: (2025)

HaPy-Bug -- Human Annotated Python Bug Resolution Dataset
by: Przymus, Piotr, et al.
Published: (2025)

Multi-Location Software Model Completion
by: Welter, Alisa, et al.
Published: (2026)

From Restructuring to Stabilization: A Large-Scale Experiment on Iterative Code Readability Refactoring with Large Language Models
by: Peitek, Norman, et al.
Published: (2026)

BugScope: Learn to Find Bugs Like Human
by: Guo, Jinyao, et al.
Published: (2025)

PanicFI: An Infrastructure for Fixing Panic Bugs in Real-World Rust Programs
by: Ni, Yunbo, et al.
Published: (2024)

Prompt Programming: A Platform for Dialogue-based Computational Problem Solving with Generative AI Models
by: Pădurean, Victor-Alexandru, et al.
Published: (2025)

HotBugs.jar: A Benchmark of Hot Fixes for Time-Critical Bugs
by: Hanna, Carol, et al.
Published: (2025)

Bugs in the Shadows: Static Detection of Faulty Python Refactorings
by: Oliveira, Jonhnanthan, et al.
Published: (2025)

GitBug-Actions: Building Reproducible Bug-Fix Benchmarks with GitHub Actions
by: Saavedra, Nuno, et al.
Published: (2023)

The Silent Scientist: When Software Research Fails to Reach Its Audience
by: Wyrich, Marvin, et al.
Published: (2025)

Harnessing Hype to Teach Empirical Thinking: An Experience With AI Coding Assistants
by: Wyrich, Marvin, et al.
Published: (2026)

Detecting Performance-Relevant Changes in Configurable Software Systems
by: Böhm, Sebastian, et al.
Published: (2025)

Software Engineering Podcasts: An Empirical Study of Their Potential as a Research Resource
by: Wyrich, Marvin, et al.
Published: (2026)

Gradient-Based Program Repair: Fixing Bugs in Continuous Program Spaces
by: Silva, André, et al.
Published: (2025)

HintPilot: LLM-based Compiler Hint Synthesis for Code Optimization
by: Jiang, Hanyun, et al.
Published: (2026)

Can LLMs Find Bugs in Code? An Evaluation from Beginner Errors to Security Vulnerabilities in Python and C++
by: Mhatre, Akshay, et al.
Published: (2025)

Fixing 7,400 Bugs for 1$: Cheap Crash-Site Program Repair
by: Zheng, Han, et al.
Published: (2025)

Understanding and Finding JIT Compiler Performance Bugs
by: Yi, Zijian, et al.
Published: (2026)

An Empirical Study on LLM-based Agents for Automated Bug Fixing
by: Meng, Xiangxin, et al.
Published: (2024)

Benchmarking Correctness and Security in Multi-Turn Code Generation
by: Rawal, Ruchit, et al.
Published: (2025)

LLM Critics Help Catch LLM Bugs
by: McAleese, Nat, et al.
Published: (2024)

HyperPUT: Generating Synthetic Faulty Programs to Challenge Bug-Finding Tools
by: Felici, Riccardo, et al.
Published: (2022)

Developers' Perception: Fixed Bugs Often Overlooked as Quality Contributions
by: Alifanov, Vitaly, et al.
Published: (2024)

HAFix: History-Augmented Large Language Models for Bug Fixing
by: Shi, Yu, et al.
Published: (2025)

Multifaceted Hero Developers and Bug-Fixing Outcomes Across Severity
by: Kumar, Amit, et al.
Published: (2026)

Characterizing Real-World Bugs in Tile Programs for Automated Bug Detection
by: Rathnasuriya, Ravishka, et al.
Published: (2026)

Finding Cross-rule Optimization Bugs in Datalog Engines
by: Zhang, Chi, et al.
Published: (2024)

Fast Fixes and Faulty Drivers: An Empirical Analysis of Regression Bug Fixing Times in the Linux Kernel
by: Ruohonen, Jukka, et al.
Published: (2024)

Empirical Research on Utilizing LLM-based Agents for Automated Bug Fixing via LangGraph
by: Wang, Jialin, et al.
Published: (2025)