How is Google using AI for internal code migrations?
Fuente:
arXiv
Saved in:
| Main Authors: | Nikolov, Stoyan, Codecasa, Daniele, Sjovall, Anna, Tabachnyk, Maxim, Chandra, Satish, Taneja, Siddharth, Ziftci, Celal |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Migrating Code At Scale With LLMs At Google
by: Ziftci, Celal, et al.
Published: (2025)
by: Ziftci, Celal, et al.
Published: (2025)
LLM-Based Automated Diagnosis Of Integration Test Failures At Google
by: Ziftci, Celal, et al.
Published: (2026)
by: Ziftci, Celal, et al.
Published: (2026)
Achieving Productivity Gains with AI-based IDE features: A Journey at Google
by: Tabachnyk, Maxim, et al.
Published: (2026)
by: Tabachnyk, Maxim, et al.
Published: (2026)
Smart Paste: Automatically Fixing Copy/Paste for Google Developers
by: Nguyen, Vincent, et al.
Published: (2025)
by: Nguyen, Vincent, et al.
Published: (2025)
Evaluating Agent-based Program Repair at Google
by: Rondon, Pat, et al.
Published: (2025)
by: Rondon, Pat, et al.
Published: (2025)
A Multi-agent AI System for Deep Learning Model Migration from TensorFlow to JAX
by: Nikolov, Stoyan, et al.
Published: (2026)
by: Nikolov, Stoyan, et al.
Published: (2026)
Agentic Bug Reproduction for Effective Automated Program Repair at Google
by: Cheng, Runxiang, et al.
Published: (2025)
by: Cheng, Runxiang, et al.
Published: (2025)
RubberDuckBench: A Benchmark for AI Coding Assistants
by: Mohammed, Ferida, et al.
Published: (2026)
by: Mohammed, Ferida, et al.
Published: (2026)
CRQBench: A Benchmark of Code Reasoning Questions
by: Dinella, Elizabeth, et al.
Published: (2024)
by: Dinella, Elizabeth, et al.
Published: (2024)
Evaluating the impact of code smell refactoring on the energy consumption of Android applications
by: Anwar, Hina, et al.
Published: (2025)
by: Anwar, Hina, et al.
Published: (2025)
Agentic Code Reasoning
by: Ugare, Shubham, et al.
Published: (2026)
by: Ugare, Shubham, et al.
Published: (2026)
How frontier AI companies could implement an internal audit function
by: Gomez, Francesca, et al.
Published: (2025)
by: Gomez, Francesca, et al.
Published: (2025)
Google Summer of Code: Student Motivations and Contributions
by: Silva, Jefferson O., et al.
Published: (2019)
by: Silva, Jefferson O., et al.
Published: (2019)
SLA-Awareness for AI-assisted coding
by: Thangarajah, Kishanthan, et al.
Published: (2025)
by: Thangarajah, Kishanthan, et al.
Published: (2025)
How do annotations affect Java code readability?
by: Guerra, Eduardo, et al.
Published: (2024)
by: Guerra, Eduardo, et al.
Published: (2024)
Empirical Analysis of Pull Requests for Google Summer of Code
by: Popoola, Saheed
Published: (2024)
by: Popoola, Saheed
Published: (2024)
Reading Between the Lines: Scalable User Feedback via Implicit Sentiment in Developer Prompts
by: Nam, Daye, et al.
Published: (2025)
by: Nam, Daye, et al.
Published: (2025)
Software Defect Prediction using Autoencoder Transformer Model
by: Barma, Seshu, et al.
Published: (2025)
by: Barma, Seshu, et al.
Published: (2025)
What a diff makes: automating code migration with large language models
by: Rosenfeld, Katherine A., et al.
Published: (2025)
by: Rosenfeld, Katherine A., et al.
Published: (2025)
Now's the Time: Computer Science Must Evolve to Emphasize Software and Systems Engineering with Artificial Intelligence (AI)
by: Sekharan, Chandra N., et al.
Published: (2026)
by: Sekharan, Chandra N., et al.
Published: (2026)
Test-Oriented Programming: rethinking coding for the GenAI era
by: Melegati, Jorge
Published: (2026)
by: Melegati, Jorge
Published: (2026)
Towards Verified Code Reasoning by LLMs
by: Sistla, Meghana, et al.
Published: (2025)
by: Sistla, Meghana, et al.
Published: (2025)
REAP: Automatic Curation of Coding Agent Benchmarks from Interactive Production Usage
by: Jha, Smriti, et al.
Published: (2026)
by: Jha, Smriti, et al.
Published: (2026)
Generating refactored code accurately using reinforcement learning
by: Palit, Indranil, et al.
Published: (2024)
by: Palit, Indranil, et al.
Published: (2024)
AI Policy, Disclosure, and Human in the Loop: How Are Contribution Guidelines Adapting to GenAI?
by: Hora, Andre, et al.
Published: (2026)
by: Hora, Andre, et al.
Published: (2026)
Evaluating the Dependency Between Cyclomatic Complexity and Response For Class
by: Stavtsev, Maxim, et al.
Published: (2024)
by: Stavtsev, Maxim, et al.
Published: (2024)
Understanding and supporting how developers prompt for LLM-powered code editing in practice
by: Nam, Daye, et al.
Published: (2025)
by: Nam, Daye, et al.
Published: (2025)
How Software Engineers Engage with AI: A Pragmatic Workflow
by: Garousi, Vahid, et al.
Published: (2025)
by: Garousi, Vahid, et al.
Published: (2025)
Can AI Agents Generate Microservices? How Far are We?
by: Adnan, Bassam, et al.
Published: (2026)
by: Adnan, Bassam, et al.
Published: (2026)
Development in times of hype: How freelancers explore Generative AI?
by: Dolata, Mateusz, et al.
Published: (2024)
by: Dolata, Mateusz, et al.
Published: (2024)
Mapping the Invisible: A Framework for Tracking COVID-19 Spread Among College Students with Google Location Data
by: Krishnan, Prajindra Sankar, et al.
Published: (2024)
by: Krishnan, Prajindra Sankar, et al.
Published: (2024)
How Students Use Generative AI for Software Testing: An Observational Study
by: Ardic, Baris, et al.
Published: (2025)
by: Ardic, Baris, et al.
Published: (2025)
An Investigation on How AI-Generated Responses Affect SoftwareEngineering Surveys
by: Santos, Ronnie de Souza, et al.
Published: (2025)
by: Santos, Ronnie de Souza, et al.
Published: (2025)
Who is using AI to code? Global diffusion and impact of generative AI
by: Daniotti, Simone, et al.
Published: (2025)
by: Daniotti, Simone, et al.
Published: (2025)
Dynamic Function Configuration and its Management in Serverless Computing: A Taxonomy and Future Directions
by: Agarwal, Siddharth, et al.
Published: (2025)
by: Agarwal, Siddharth, et al.
Published: (2025)
Test code generation at Ericsson using Program Analysis Augmented Fine Tuned LLMs
by: Krishna, Sai, et al.
Published: (2025)
by: Krishna, Sai, et al.
Published: (2025)
Prioritizing App Reviews for Developer Responses on Google Play
by: Jafari, Mohsen, et al.
Published: (2025)
by: Jafari, Mohsen, et al.
Published: (2025)
Code with Me or for Me? How Increasing AI Automation Transforms Developer Workflows
by: Chen, Valerie, et al.
Published: (2025)
by: Chen, Valerie, et al.
Published: (2025)
How far are AI-powered programming assistants from meeting developers' needs?
by: Tan, Xin, et al.
Published: (2024)
by: Tan, Xin, et al.
Published: (2024)
How do Humans and LLMs Process Confusing Code?
by: Abdelsalam, Youssef, et al.
Published: (2025)
by: Abdelsalam, Youssef, et al.
Published: (2025)
Similar Items
-
Migrating Code At Scale With LLMs At Google
by: Ziftci, Celal, et al.
Published: (2025) -
LLM-Based Automated Diagnosis Of Integration Test Failures At Google
by: Ziftci, Celal, et al.
Published: (2026) -
Achieving Productivity Gains with AI-based IDE features: A Journey at Google
by: Tabachnyk, Maxim, et al.
Published: (2026) -
Smart Paste: Automatically Fixing Copy/Paste for Google Developers
by: Nguyen, Vincent, et al.
Published: (2025) -
Evaluating Agent-based Program Repair at Google
by: Rondon, Pat, et al.
Published: (2025)