Cover Image

Improving Code Reviewer Recommendation: Accuracy, Latency, Workload, and Bystanders

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Rigby, Peter C., Rogers, Seth, Saleem, Sadruddin, Suresh, Parth, Suskin, Daniel, Riggs, Patrick, Maddila, Chandra, Nagappan, Nachiappan
Format:	Preprint
Published:	2023
Subjects:	Software Engineering
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

AI-Assisted Fixes to Code Review Comments at Scale
by: Maddila, Chandra, et al.
Published: (2025)

AI-assisted Code Authoring at Scale: Fine-tuning, deploying, and mixed methods evaluation
by: Murali, Vijayaraghavan, et al.
Published: (2023)

Multi-line AI-assisted Code Authoring
by: Dunay, Omer, et al.
Published: (2024)

Moving Faster and Reducing Risk: Using LLMs in Release Deployment
by: Abreu, Rui, et al.
Published: (2024)

AI-Assisted SQL Authoring at Industry Scale
by: Maddila, Chandra, et al.
Published: (2024)

Balancing Latency and Accuracy of Code Completion via Local-Cloud Model Cascading
by: Lu, Hanzhen, et al.
Published: (2026)

REAP: Automatic Curation of Coding Agent Benchmarks from Interactive Production Usage
by: Jha, Smriti, et al.
Published: (2026)

Code Improvement Practices at Meta
by: Mockus, Audris, et al.
Published: (2025)

Enhancing LLM Code Generation: A Systematic Evaluation of Multi-Agent Collaboration and Runtime Debugging for Improved Accuracy, Reliability, and Latency
by: Ashrafi, Nazmus, et al.
Published: (2025)

Test-Driven Development for Code Generation
by: Mathews, Noble Saji, et al.
Published: (2024)

Examining LLMs Ability to Summarize Code Through Mutation-Analysis
by: Khatib, Lara, et al.
Published: (2026)

Agentic Program Repair from Test Failures at Scale: A Neuro-symbolic approach with static analysis and test execution feedback
by: Maddila, Chandra, et al.
Published: (2025)

Is GitHub's Copilot as Bad as Humans at Introducing Vulnerabilities in Code?
by: Asare, Owura, et al.
Published: (2022)

Measuring the Runtime Performance of C++ Code Written by Humans using GitHub Copilot
by: Erhabor, Daniel, et al.
Published: (2023)

Wink: Recovering from Misbehaviors in Coding Agents
by: Nanda, Rahul, et al.
Published: (2026)

Whodunit: Classifying Code as Human Authored or GPT-4 Generated -- A case study on CodeChef problems
by: Idialu, Oseremen Joy, et al.
Published: (2024)

Is Your Automated Software Engineer Trustworthy?
by: Mathews, Noble Saji, et al.
Published: (2025)

AI-Generated Smells: An Analysis of Code and Architecture in LLM and Agent-Driven Development
by: Zhu, Yuecai, et al.
Published: (2026)

Checker Bug Detection and Repair in Deep Learning Libraries
by: Harzevili, Nima Shiri, et al.
Published: (2024)

iCodeReviewer: Improving Secure Code Review with Mixture of Prompts
by: Peng, Yun, et al.
Published: (2025)

Code Reviewer Recommendation Based on a Hypergraph with Multiplex Relationships
by: Qiao, Yu, et al.
Published: (2024)

AssertFlip: Reproducing Bugs via Inversion of LLM-Generated Passing Tests
by: Khatib, Lara, et al.
Published: (2025)

Does SWE-Bench-Verified Test Agent Ability or Model Memory?
by: Prathifkumar, Thanosan, et al.
Published: (2025)

Articulate but Wrong: Self-Review Failures in LLM-Based Code Modernization
by: Reddy, Gokul Chandra Purnachandra, et al.
Published: (2026)

Studying Quality Improvements Recommended via Manual and Automated Code Review
by: Crupi, Giuseppe, et al.
Published: (2026)

Understanding Web Application Workloads and Their Applications: Systematic Literature Review and Characterization
by: Aghili, Roozbeh, et al.
Published: (2024)

Improving Automated Code Reviews: Learning from Experience
by: Lin, Hong Yi, et al.
Published: (2024)

Design choices made by LLM-based test generators prevent them from finding bugs
by: Mathews, Noble Saji, et al.
Published: (2024)

Understanding the Human-LLM Dynamic: A Literature Survey of LLM Use in Programming Tasks
by: Etsenake, Deborah, et al.
Published: (2024)

Be a Partner, not a Bystander in Software Engineering Practice: Bridging the Gaps between Academia and Industry
by: Rahman, Mohammad Masudur, et al.
Published: (2026)

Improving Automated Secure Code Reviews: A Synthetic Dataset for Code Vulnerability Flaws
by: Centellas-Claros, Leonardo, et al.
Published: (2025)

A User-centered Security Evaluation of Copilot
by: Asare, Owura, et al.
Published: (2023)

RLocator: Reinforcement Learning for Bug Localization
by: Chakraborty, Partha, et al.
Published: (2023)

Improving the Learning of Code Review Successive Tasks with Cross-Task Knowledge Distillation
by: Sghaier, Oussama Ben, et al.
Published: (2024)

CRQBench: A Benchmark of Code Reasoning Questions
by: Dinella, Elizabeth, et al.
Published: (2024)

Automatically Recommend Code Updates: Are We There Yet?
by: Liu, Yue, et al.
Published: (2022)

Quo Vadis, Code Review? Exploring the Future of Code Review
by: Dorner, Michael, et al.
Published: (2025)

Self-reflection in Automated Qualitative Coding: Improving Text Annotation through Secondary LLM Critique
by: Dunivin, Zackary Okun, et al.
Published: (2026)

Changes in Coding Behavior and Performance Since the Introduction of LLMs
by: Zhang, Yufan, et al.
Published: (2026)

Static Code Analyzer Recommendation via Preference Mining
by: Ge, Xiuting, et al.
Published: (2024)