Understanding Practitioners' Expectations on Clear Code Review Comments
Fuente:
arXiv
Saved in:
| Main Authors: | Chen, Junkai, Li, Zhenhao, Mao, Qiheng, Hu, Xing, Liu, Kui, Xia, Xin |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Towards Explainable Vulnerability Detection with Large Language Models
by: Mao, Qiheng, et al.
Published: (2024)
by: Mao, Qiheng, et al.
Published: (2024)
NLPerturbator: Studying the Robustness of Code LLMs to Natural Language Variations
by: Chen, Junkai, et al.
Published: (2024)
by: Chen, Junkai, et al.
Published: (2024)
An Empirical Study of Retrieval-Augmented Code Generation: Challenges and Opportunities
by: Yang, Zezhou, et al.
Published: (2025)
by: Yang, Zezhou, et al.
Published: (2025)
A Roadmap on Modern Code Review: Challenges and Opportunities
by: Yang, Zezhou, et al.
Published: (2024)
by: Yang, Zezhou, et al.
Published: (2024)
Reasoning Runtime Behavior of a Program with LLM: How Far Are We?
by: Chen, Junkai, et al.
Published: (2024)
by: Chen, Junkai, et al.
Published: (2024)
An Empirical Study of Speculative Decoding on Software Engineering Tasks
by: Li, Yijia, et al.
Published: (2026)
by: Li, Yijia, et al.
Published: (2026)
Open the Oyster: Empirical Evaluation and Improvement of Code Reasoning Confidence in LLMs
by: Wang, Shufan, et al.
Published: (2025)
by: Wang, Shufan, et al.
Published: (2025)
Practitioners' Expectations on Log Anomaly Detection
by: Ma, Xiaoxue, et al.
Published: (2024)
by: Ma, Xiaoxue, et al.
Published: (2024)
A Benchmark for Evaluating Repository-Level Code Agents with Intermediate Reasoning on Feature Addition Task
by: Liu, Shuhan, et al.
Published: (2026)
by: Liu, Shuhan, et al.
Published: (2026)
ActRef: Enhancing the Understanding of Python Code Refactoring with Action-Based Analysis
by: Wang, Siqi, et al.
Published: (2025)
by: Wang, Siqi, et al.
Published: (2025)
Understanding Emojis :) in Useful Code Review Comments
by: Ahmed, Sharif, et al.
Published: (2024)
by: Ahmed, Sharif, et al.
Published: (2024)
Similar but Patched Code Considered Harmful -- The Impact of Similar but Patched Code on Recurring Vulnerability Detection and How to Remove Them
by: Tan, Zixuan, et al.
Published: (2024)
by: Tan, Zixuan, et al.
Published: (2024)
Defects4Log: Benchmarking LLMs for Logging Code Defect Detection and Reasoning
by: Wang, Xin, et al.
Published: (2025)
by: Wang, Xin, et al.
Published: (2025)
Automating Comment Generation for Smart Contract from Bytecode
by: Xiang, Jianhang, et al.
Published: (2025)
by: Xiang, Jianhang, et al.
Published: (2025)
Deep Learning Based Code Generation Methods: Literature Review
by: Yang, Zezhou, et al.
Published: (2023)
by: Yang, Zezhou, et al.
Published: (2023)
iCodeReviewer: Improving Secure Code Review with Mixture of Prompts
by: Peng, Yun, et al.
Published: (2025)
by: Peng, Yun, et al.
Published: (2025)
Where Is Self-admitted Code Generated by Large Language Models on GitHub?
by: Yu, Xiao, et al.
Published: (2024)
by: Yu, Xiao, et al.
Published: (2024)
Learning in the Wild: Towards Leveraging Unlabeled Data for Effectively Tuning Pre-trained Code Models
by: Gao, Shuzheng, et al.
Published: (2024)
by: Gao, Shuzheng, et al.
Published: (2024)
ZeroCoder: Can LLMs Improve Code Generation Without Ground-Truth Supervision?
by: Fan, Lishui, et al.
Published: (2026)
by: Fan, Lishui, et al.
Published: (2026)
Assessing and Advancing Benchmarks for Evaluating Large Language Models in Software Engineering Tasks
by: Hu, Xing, et al.
Published: (2025)
by: Hu, Xing, et al.
Published: (2025)
Challenges of Using Pre-trained Models: the Practitioners' Perspective
by: Tan, Xin, et al.
Published: (2024)
by: Tan, Xin, et al.
Published: (2024)
Understanding Code Understandability Improvements in Code Reviews
by: Oliveira, Delano, et al.
Published: (2024)
by: Oliveira, Delano, et al.
Published: (2024)
Semantic Consensus Decoding: Backdoor Defense for Verilog Code Generation
by: Yang, Guang, et al.
Published: (2026)
by: Yang, Guang, et al.
Published: (2026)
Retrieval-Augmented Code Review Comment Generation
by: Hong, Hyunsun, et al.
Published: (2025)
by: Hong, Hyunsun, et al.
Published: (2025)
CATCODER: Repository-Level Code Generation with Relevant Code and Type Context
by: Pan, Zhiyuan, et al.
Published: (2024)
by: Pan, Zhiyuan, et al.
Published: (2024)
SelfPiCo: Self-Guided Partial Code Execution with LLMs
by: Xue, Zhipeng, et al.
Published: (2024)
by: Xue, Zhipeng, et al.
Published: (2024)
CREME: Robustness Enhancement of Code LLMs via Layer-Aware Model Editing
by: Liu, Shuhan, et al.
Published: (2025)
by: Liu, Shuhan, et al.
Published: (2025)
From Mirage to Grounding: Towards Reliable Multimodal Circuit-to-Verilog Code Generation
by: Yang, Guang, et al.
Published: (2026)
by: Yang, Guang, et al.
Published: (2026)
Demystifying Code Snippets in Code Reviews: A Study of the OpenStack and Qt Communities and A Practitioner Survey
by: Zhang, Beiqi, et al.
Published: (2023)
by: Zhang, Beiqi, et al.
Published: (2023)
SeRe: A Security-Related Code Review Dataset Aligned with Real-World Review Activities
by: Zhao, Zixiao, et al.
Published: (2026)
by: Zhao, Zixiao, et al.
Published: (2026)
Fight Fire with Fire: How Much Can We Trust ChatGPT on Source Code-Related Tasks?
by: Yu, Xiao, et al.
Published: (2024)
by: Yu, Xiao, et al.
Published: (2024)
Towards Understanding Bugs in Distributed Training and Inference Frameworks for Large Language Models
by: Yu, Xiao, et al.
Published: (2025)
by: Yu, Xiao, et al.
Published: (2025)
Deep Assessment of Code Review Generation Approaches: Beyond Lexical Similarity
by: Jiang, Yanjie, et al.
Published: (2025)
by: Jiang, Yanjie, et al.
Published: (2025)
Too Noisy To Learn: Enhancing Data Quality for Code Review Comment Generation
by: Liu, Chunhua, et al.
Published: (2025)
by: Liu, Chunhua, et al.
Published: (2025)
Leveraging Reviewer Experience in Code Review Comment Generation
by: Lin, Hong Yi, et al.
Published: (2024)
by: Lin, Hong Yi, et al.
Published: (2024)
LLM4Perf: Large Language Models Are Effective Samplers for Multi-Objective Performance Modeling
by: Wang, Xin, et al.
Published: (2025)
by: Wang, Xin, et al.
Published: (2025)
Large Language Models for Code Generation: The Practitioners Perspective
by: Rasheed, Zeeshan, et al.
Published: (2025)
by: Rasheed, Zeeshan, et al.
Published: (2025)
Clean Code, Better Models: Enhancing LLM Performance with Smell-Cleaned Dataset
by: Xue, Zhipeng, et al.
Published: (2025)
by: Xue, Zhipeng, et al.
Published: (2025)
Re-Evaluating Code LLM Benchmarks Under Semantic Mutation
by: Pan, Zhiyuan, et al.
Published: (2025)
by: Pan, Zhiyuan, et al.
Published: (2025)
Refactoring Deep Learning Code: A Study of Practices and Unsatisfied Tool Needs
by: Wang, Siqi, et al.
Published: (2024)
by: Wang, Siqi, et al.
Published: (2024)
Similar Items
-
Towards Explainable Vulnerability Detection with Large Language Models
by: Mao, Qiheng, et al.
Published: (2024) -
NLPerturbator: Studying the Robustness of Code LLMs to Natural Language Variations
by: Chen, Junkai, et al.
Published: (2024) -
An Empirical Study of Retrieval-Augmented Code Generation: Challenges and Opportunities
by: Yang, Zezhou, et al.
Published: (2025) -
A Roadmap on Modern Code Review: Challenges and Opportunities
by: Yang, Zezhou, et al.
Published: (2024) -
Reasoning Runtime Behavior of a Program with LLM: How Far Are We?
by: Chen, Junkai, et al.
Published: (2024)