Copertina

Mint: Cost-Efficient Tracing with All Requests Collection via Commonality and Variability Analysis

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Huang, Haiyu, Chen, Cheng, Chen, Kunyi, Chen, Pengfei, Yu, Guangba, He, Zilong, Wang, Yilun, Zhang, Huxing, Zhou, Qi
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Software Engineering
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

FaaSRCA: Full Lifecycle Root Cause Analysis for Serverless Applications
di: Huang, Jin, et al.
Pubblicazione: (2024)

Cloud-OpsBench: A Reproducible Benchmark for Agentic Root Cause Analysis in Cloud Systems
di: Wang, Yilun, et al.
Pubblicazione: (2026)

InferLog: Accelerating LLM Inference for Online Log Parsing via ICL-oriented Prefix Caching
di: Wang, Yilun, et al.
Pubblicazione: (2025)

DaiFu: In-Situ Crash Recovery for Deep Learning Systems
di: He, Zilong, et al.
Pubblicazione: (2025)

WakeMint: Detecting Sleepminting Vulnerabilities in NFT Smart Contracts
di: Xiao, Lei, et al.
Pubblicazione: (2025)

Trace Sampling 2.0: Code Knowledge Enhanced Span-level Sampling for Distributed Tracing
di: Wu, Yulun, et al.
Pubblicazione: (2025)

Tracezip: Efficient Distributed Tracing via Trace Compression
di: Chen, Zhuangbin, et al.
Pubblicazione: (2025)

COCA: Generative Root Cause Analysis for Distributed Systems with Code Knowledge
di: Li, Yichen, et al.
Pubblicazione: (2025)

A Survey on Failure Analysis and Fault Injection in AI Systems
di: Yu, Guangba, et al.
Pubblicazione: (2024)

KPIRoot+: An Efficient Integrated Framework for Anomaly Detection and Root Cause Analysis in Large-Scale Cloud Systems
di: Gu, Wenwei, et al.
Pubblicazione: (2025)

LIDL: LLM Integration Defect Localization via Knowledge Graph-Enhanced Multi-Agent Analysis
di: Tan, Gou, et al.
Pubblicazione: (2026)

Let's Make Every Pull Request Meaningful: An Empirical Analysis of Developer and Agentic Pull Requests
di: Yoshioka, Haruhiko, et al.
Pubblicazione: (2026)

Adaptive Request Scheduling for CodeLLM Serving with SLA Guarantees
di: Chang, Shi, et al.
Pubblicazione: (2025)

AlertGuardian: Intelligent Alert Life-Cycle Management for Large-scale Cloud Systems
di: Yu, Guangba, et al.
Pubblicazione: (2026)

DocuMint: Docstring Generation for Python using Small Language Models
di: Poudel, Bibek, et al.
Pubblicazione: (2024)

Feature Request Analysis and Processing: Tasks, Techniques, and Trends
di: Niu, Feifei, et al.
Pubblicazione: (2025)

Empirical Analysis of Pull Requests for Google Summer of Code
di: Popoola, Saheed
Pubblicazione: (2024)

CODEPROMPTZIP: Code-specific Prompt Compression for Retrieval-Augmented Generation in Coding Tasks with LMs
di: He, Pengfei, et al.
Pubblicazione: (2025)

SLICET5: Static Program Slicing using Language Models with Copy Mechanism and Constrained Decoding
di: He, Pengfei, et al.
Pubblicazione: (2025)

FastCode: Fast and Cost-Efficient Code Understanding and Reasoning
di: Li, Zhonghang, et al.
Pubblicazione: (2026)

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software
di: KC, Pragyan, et al.
Pubblicazione: (2025)

Evaluating the Effectiveness and Efficiency of Demonstration Retrievers in RAG for Coding Tasks
di: He, Pengfei, et al.
Pubblicazione: (2024)

The Value of Effective Pull Request Description
di: Pirouzkhah, Shirin, et al.
Pubblicazione: (2026)

On Unified Prompt Tuning for Request Quality Assurance in Public Code Review
di: Chen, Xinyu, et al.
Pubblicazione: (2024)

Change And Cover: Last-Mile, Pull Request-Based Regression Test Augmentation
di: Zhou, Zitong, et al.
Pubblicazione: (2026)

Comparing AI Coding Agents: A Task-Stratified Analysis of Pull Request Acceptance
di: Pinna, Giovanni, et al.
Pubblicazione: (2026)

EET: Experience-Driven Early Termination for Cost-Efficient Software Engineering Agents
di: Guo, Yaoqi, et al.
Pubblicazione: (2026)

LogPilot: Intent-aware and Scalable Alert Diagnosis for Large-scale Online Service Systems
di: Jiang, Zhihan, et al.
Pubblicazione: (2025)

Knowledge-Guided Prompt Learning for Request Quality Assurance in Public Code Review
di: Li, Lin, et al.
Pubblicazione: (2024)

Mut4All: Fuzzing Compilers via LLM-Synthesized Mutators Learned from Bug Reports
di: Wang, Bo, et al.
Pubblicazione: (2025)

Why Does the LLM Stop Computing: An Empirical Study of User-Reported Failures in Open-Source LLMs
di: Yu, Guangba, et al.
Pubblicazione: (2026)

Trace: Securing Smart Contract Repository Against Access Control Vulnerability
di: Chen, Chong, et al.
Pubblicazione: (2025)

L4: Diagnosing Large-scale LLM Training Failures via Automated Log Analysis
di: Jiang, Zhihan, et al.
Pubblicazione: (2025)

Towards Trustworthy LLMs for Code: A Data-Centric Synergistic Auditing Framework
di: Wang, Chong, et al.
Pubblicazione: (2024)

Larger Is Not Always Better: Exploring Small Open-source Language Models in Logging Statement Generation
di: Zhong, Renyi, et al.
Pubblicazione: (2025)

A Longitudinal Analysis of Good First Issue Practices and Newcomer Pull Requests in Popular OSS Projects
di: Hoshikawa, Hirotatsu, et al.
Pubblicazione: (2026)

The Death Spiral of Open Source Projects: A Post-Mortem Analysis of Pull Request Workflow Dynamics
di: Kaushik, Mohit, et al.
Pubblicazione: (2026)

GitHub Actions: The Impact on the Pull Request Process
di: Wessel, Mairieli, et al.
Pubblicazione: (2022)

Insights into Security-Related AI-Generated Pull Requests
di: Rabbi, Md Fazle, et al.
Pubblicazione: (2026)

The Impact of Documentation on Test Engagement in Pull Requests in OSS
di: Amore, Teal, et al.
Pubblicazione: (2026)