activity
20242026
most citedNatural Language Outlines for Code: Literate Programming in the LLM Era

8 citations · 10 across the 8 of their papers we have counts for

collaborators

8 papers

cs.SE2026

REAP: Automatic Curation of Coding Agent Benchmarks from Interactive Production Usage

Smriti Jha, Matteo Paltenghi, Chandra Maddila +3

Production deployment of AI coding agents requires fast, reproducible evaluation signals. Existing industrial practices trade off speed and fidelity: online A/B testing takes weeks…

cs.SE2026

Agentic Code Reasoning

Shubham Ugare, Satish Chandra

Can LLM agents explore codebases and reason about code semantics without executing the code? We study this capability, which we call agentic code reasoning, and introduce semi-form…

cs.SE2025

Abstain and Validate: A Dual-LLM Policy for Reducing Noise in Agentic Program Repair

José Cambronero, Michele Tufano, Sherry Shi +7

Agentic Automated Program Repair (APR) is increasingly tackling complex, repository-level bugs in industry, but ultimately these patches still need to be reviewed by a human before…

cs.SE2025

Towards Verified Code Reasoning by LLMs

Meghana Sistla, Gogul Balakrishnan, Pat Rondon +3

While LLM-based agents are able to tackle a wide variety of code reasoning questions, the answers are not always correct. This prevents the agent from being useful in situations wh…

cs.SE2025★ 1 cited

Agentic Bug Reproduction for Effective Automated Program Repair at Google

Runxiang Cheng, Michele Tufano, Jürgen Cito +5

Bug reports often lack sufficient detail for developers to reproduce and fix the underlying defects. Bug Reproduction Tests (BRTs), tests that fail when the bug is present and pass…

cs.SE2025

Evaluating Agent-based Program Repair at Google

Pat Rondon, Renyao Wei, José Cambronero +5

Agent-based program repair offers to automatically resolve complex bugs end-to-end by combining the planning, tool use, and code generation abilities of modern LLMs. Recent work ha…

cs.SE2025★ 1 cited

How is Google using AI for internal code migrations?

Stoyan Nikolov, Daniele Codecasa, Anna Sjovall +4

In recent years, there has been a tremendous interest in using generative AI, and particularly large language models (LLMs) in software engineering; indeed there are now several co…

cs.SE2024★ 8 cited

Natural Language Outlines for Code: Literate Programming in the LLM Era

Kensen Shi, Deniz Altınbüken, Saswat Anand +17

We propose using natural language outlines as a novel modality and interaction surface for providing AI assistance to developers throughout the software development process. An NL…