Saved in:
| Main Authors: | Ma, Xiaoxue, Zou, Huiqi, He, Pinjia, Keung, Jacky, Li, Yishu, Yu, Xiao, Sarro, Federica |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.03489 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Practitioners' Expectations on Log Anomaly Detection
by: Ma, Xiaoxue, et al.
Published: (2024)
by: Ma, Xiaoxue, et al.
Published: (2024)
A Comprehensive Study of Bugs in Modern Distributed Deep Learning Systems
by: Ma, Xiaoxue, et al.
Published: (2025)
by: Ma, Xiaoxue, et al.
Published: (2025)
R2Code: A Self-Reflective LLM Framework for Requirements-to-Code Traceability
by: Wang, Yifei, et al.
Published: (2026)
by: Wang, Yifei, et al.
Published: (2026)
UniAda: Universal Adaptive Multi-objective Adversarial Attack for End-to-End Autonomous Driving Systems
by: Zhang, Jingyu, et al.
Published: (2026)
by: Zhang, Jingyu, et al.
Published: (2026)
FedLAD: A Modular and Adaptive Testbed for Federated Log Anomaly Detection
by: Liao, Yihan, et al.
Published: (2025)
by: Liao, Yihan, et al.
Published: (2025)
An Empirical Study of Perceptions of General LLMs and Multimodal LLMs on Hugging Face
by: Liu, Yujian, et al.
Published: (2026)
by: Liu, Yujian, et al.
Published: (2026)
Data Preparation for Deep Learning based Code Smell Detection: A Systematic Literature Review
by: Zhang, Fengji, et al.
Published: (2024)
by: Zhang, Fengji, et al.
Published: (2024)
Exposing and Defending Membership Leakage in Vulnerability Prediction Models
by: Liao, Yihan, et al.
Published: (2025)
by: Liao, Yihan, et al.
Published: (2025)
Understanding Fairness in Software Engineering: Insights from Stack Exchange
by: Sesari, Emeralda, et al.
Published: (2024)
by: Sesari, Emeralda, et al.
Published: (2024)
BayesInsights: Modelling Software Delivery and Developer Experience with Bayesian Networks at Bloomberg
by: Kirbas, Serkan, et al.
Published: (2026)
by: Kirbas, Serkan, et al.
Published: (2026)
Empirical Insights of Test Selection Metrics under Multiple Testing Objectives and Distribution Shifts
by: Zhang, Jingyu, et al.
Published: (2026)
by: Zhang, Jingyu, et al.
Published: (2026)
Impact of Log Parsing on Deep Learning-Based Anomaly Detection
by: Khan, Zanis Ali, et al.
Published: (2023)
by: Khan, Zanis Ali, et al.
Published: (2023)
AL-Bench: A Benchmark for Automatic Logging
by: Tan, Boyin, et al.
Published: (2025)
by: Tan, Boyin, et al.
Published: (2025)
Unlocking the Power of Numbers: Log Compression via Numeric Token Parsing
by: Yu, Siyu, et al.
Published: (2024)
by: Yu, Siyu, et al.
Published: (2024)
Prompting for Automatic Log Template Extraction
by: Xu, Junjielong, et al.
Published: (2023)
by: Xu, Junjielong, et al.
Published: (2023)
Lightweight Model Editing for LLMs to Correct Deprecated API Recommendations
by: Lin, Guancheng, et al.
Published: (2025)
by: Lin, Guancheng, et al.
Published: (2025)
LogPTR: Variable-Aware Log Parsing with Pointer Network
by: Wu, Yifan, et al.
Published: (2024)
by: Wu, Yifan, et al.
Published: (2024)
Automated Repair of Ambiguous Problem Descriptions for LLM-Based Code Generation
by: Jia, Haoxiang, et al.
Published: (2025)
by: Jia, Haoxiang, et al.
Published: (2025)
AnomalyGen: Enhancing Log-Based Anomaly Detection with Code-Guided Data Augmentation
by: Li, Xinyu, et al.
Published: (2026)
by: Li, Xinyu, et al.
Published: (2026)
Psychological Safety Framework in Pull-based Open Source Projects
by: Sesari, Emeralda, et al.
Published: (2025)
by: Sesari, Emeralda, et al.
Published: (2025)
It is Giving Major Satisfaction: Why Fairness Matters for Software Practitioners
by: Sesari, Emeralda, et al.
Published: (2024)
by: Sesari, Emeralda, et al.
Published: (2024)
LLM-Based Misconfiguration Detection for AWS Serverless Computing
by: Wen, Jinfeng, et al.
Published: (2024)
by: Wen, Jinfeng, et al.
Published: (2024)
When Fine-Tuning LLMs Meets Data Privacy: An Empirical Study of Federated Learning in LLM-Based Program Repair
by: Luo, Wenqiang, et al.
Published: (2024)
by: Luo, Wenqiang, et al.
Published: (2024)
Environment-in-the-Loop: Rethinking Code Migration with LLM-based Agents
by: Li, Xiang, et al.
Published: (2026)
by: Li, Xiang, et al.
Published: (2026)
MicLog: Towards Accurate and Efficient LLM-based Log Parsing via Progressive Meta In-Context Learning
by: Yu, Jianbo, et al.
Published: (2026)
by: Yu, Jianbo, et al.
Published: (2026)
ZeroLog: Zero-Label Generalizable Cross-System Log-based Anomaly Detection
by: Zhao, Xinlong, et al.
Published: (2025)
by: Zhao, Xinlong, et al.
Published: (2025)
Generative AI for Testing of Autonomous Driving Systems: A Survey
by: Song, Qunying, et al.
Published: (2025)
by: Song, Qunying, et al.
Published: (2025)
Unveiling Practical Shortcomings of Patch Overfitting Detection Techniques
by: Williams, David, et al.
Published: (2026)
by: Williams, David, et al.
Published: (2026)
Go Static: Contextualized Logging Statement Generation
by: Li, Yichen, et al.
Published: (2024)
by: Li, Yichen, et al.
Published: (2024)
Hot Fixing Software: A Comprehensive Review of Terminology, Techniques, and Applications
by: Hanna, Carol, et al.
Published: (2024)
by: Hanna, Carol, et al.
Published: (2024)
Unveiling Overlooked Performance Variance in Serverless Computing
by: Wen, Jinfeng, et al.
Published: (2023)
by: Wen, Jinfeng, et al.
Published: (2023)
HotBugs.jar: A Benchmark of Hot Fixes for Time-Critical Bugs
by: Hanna, Carol, et al.
Published: (2025)
by: Hanna, Carol, et al.
Published: (2025)
Comparing AI Coding Agents: A Task-Stratified Analysis of Pull Request Acceptance
by: Pinna, Giovanni, et al.
Published: (2026)
by: Pinna, Giovanni, et al.
Published: (2026)
From Research to Practice: An Interactive Rapid Review of Autonomous Driving System Testing in Industry
by: Song, Qunying, et al.
Published: (2026)
by: Song, Qunying, et al.
Published: (2026)
Exploring the Effectiveness of LLMs in Automated Logging Generation: An Empirical Study
by: Li, Yichen, et al.
Published: (2023)
by: Li, Yichen, et al.
Published: (2023)
HerAgent: Rethinking the Automated Environment Deployment via Hierarchical Test Pyramid
by: Li, Xiang, et al.
Published: (2026)
by: Li, Xiang, et al.
Published: (2026)
LLM meets ML: Data-efficient Anomaly Detection on Unstable Logs
by: Hadadi, Fatemeh, et al.
Published: (2024)
by: Hadadi, Fatemeh, et al.
Published: (2024)
Delving into Parameter-Efficient Fine-Tuning in Code Change Learning: An Empirical Study
by: Liu, Shuo, et al.
Published: (2024)
by: Liu, Shuo, et al.
Published: (2024)
LogSage: An LLM-Based Framework for CI/CD Failure Detection and Remediation with Industrial Validation
by: Xu, Weiyuan, et al.
Published: (2025)
by: Xu, Weiyuan, et al.
Published: (2025)
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)
Similar Items
-
Practitioners' Expectations on Log Anomaly Detection
by: Ma, Xiaoxue, et al.
Published: (2024) -
A Comprehensive Study of Bugs in Modern Distributed Deep Learning Systems
by: Ma, Xiaoxue, et al.
Published: (2025) -
R2Code: A Self-Reflective LLM Framework for Requirements-to-Code Traceability
by: Wang, Yifei, et al.
Published: (2026) -
UniAda: Universal Adaptive Multi-objective Adversarial Attack for End-to-End Autonomous Driving Systems
by: Zhang, Jingyu, et al.
Published: (2026) -
FedLAD: A Modular and Adaptive Testbed for Federated Log Anomaly Detection
by: Liao, Yihan, et al.
Published: (2025)