Copertina

Automated File-Level Logging Generation for Machine Learning Applications using LLMs: A Case Study using GPT-4o Mini

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Rodriguez, Mayra Sofia Ruiz, Khatoonabadi, SayedHassan, Shihab, Emad
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Software Engineering Artificial Intelligence Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Evaluating the Use of LLMs for Automated DOM-Level Resolution of Web Performance Issues
di: Peters, Gideon, et al.
Pubblicazione: (2026)

How Robust are LLM-Generated Library Imports? An Empirical Study using Stack Overflow
di: Latendresse, Jasmine, et al.
Pubblicazione: (2025)

Evaluating the Use of LLMs for Documentation to Code Traceability
di: Alor, Ebube, et al.
Pubblicazione: (2025)

Beyond Synthetic Benchmarks: Evaluating LLM Performance on Real-World Class-Level Code Generation
di: Rahman, Musfiqur, et al.
Pubblicazione: (2025)

Is ChatGPT a Good Software Librarian? An Exploratory Study on the Use of ChatGPT for Software Library Recommendations
di: Latendresse, Jasmine, et al.
Pubblicazione: (2024)

Synergizing LLMs and Knowledge Graphs: A Novel Approach to Software Repository-Related Question Answering
di: Abedu, Samuel, et al.
Pubblicazione: (2024)

OpenClassGen: A Large-Scale Corpus of Real-World Python Classes for LLM Research
di: Rahman, Musfiqur, et al.
Pubblicazione: (2025)

Automatic Detection of LLM-Generated Code: A Comparative Case Study of Contemporary Models Across Function and Class Granularities
di: Rahman, Musfiqur, et al.
Pubblicazione: (2024)

Tokenomics: Quantifying Where Tokens Are Used in Agentic Software Engineering
di: Salim, Mohamad, et al.
Pubblicazione: (2026)

The Impact of Large Language Models (LLMs) on Code Review Process
di: Collante, Antonio, et al.
Pubblicazione: (2025)

Understanding the Helpfulness of Stale Bot for Pull-based Development: An Empirical Study of 20 Large Open-Source Projects
di: Khatoonabadi, SayedHassan, et al.
Pubblicazione: (2023)

An Approach for Auto Generation of Labeling Functions for Software Engineering Chatbots
di: Alor, Ebube, et al.
Pubblicazione: (2024)

On Wasted Contributions: Understanding the Dynamics of Contributor-Abandoned Pull Requests
di: Khatoonabadi, SayedHassan, et al.
Pubblicazione: (2021)

Predicting the First Response Latency of Maintainers and Contributors in Pull Requests
di: Khatoonabadi, SayedHassan, et al.
Pubblicazione: (2023)

The Impact of Environment Configurations on the Stability of AI-Enabled Systems
di: Rahman, Musfiqur, et al.
Pubblicazione: (2024)

A Machine Learning-Based Approach For Detecting Malicious PyPI Packages
di: Samaana, Haya, et al.
Pubblicazione: (2024)

Will It Survive? Deciphering the Fate of AI-Generated Code in Open Source
di: Rahman, Musfiqur, et al.
Pubblicazione: (2026)

Exploring the Effectiveness of LLMs in Automated Logging Generation: An Empirical Study
di: Li, Yichen, et al.
Pubblicazione: (2023)

Opportunities and Security Risks of Technical Leverage: A Replication Study on the NPM Ecosystem
di: Samaana, Haya, et al.
Pubblicazione: (2024)

An Analysis of Automated Use Case Component Extraction from Scenarios using ChatGPT
di: KC, Pragyan, et al.
Pubblicazione: (2024)

LILAC: Log Parsing using LLMs with Adaptive Parsing Cache
di: Jiang, Zhihan, et al.
Pubblicazione: (2023)

A Transformer-based Approach for Augmenting Software Engineering Chatbots Datasets
di: Abdellatif, Ahmad, et al.
Pubblicazione: (2024)

Single-Language Evidence Is Insufficient for Automated Logging: A Multilingual Benchmark and Empirical Study with LLMs
di: Zhong, Renyi, et al.
Pubblicazione: (2026)

Defects4Log: Benchmarking LLMs for Logging Code Defect Detection and Reasoning
di: Wang, Xin, et al.
Pubblicazione: (2025)

Log Parsing using LLMs with Self-Generated In-Context Learning and Self-Correction
di: Wu, Yifan, et al.
Pubblicazione: (2024)

Studying How Configurations Impact Code Generation in LLMs: the Case of ChatGPT
di: Donato, Benedetta, et al.
Pubblicazione: (2025)

Leveraging LLMs for Multi-File DSL Code Generation: An Industrial Case Study
di: Chand, Sivajeet, et al.
Pubblicazione: (2026)

LogBabylon: A Unified Framework for Cross-Log File Integration and Analysis
di: Karanjai, Rabimba, et al.
Pubblicazione: (2024)

Automated Root Causing of Cloud Incidents using In-Context Learning with GPT-4
di: Zhang, Xuchao, et al.
Pubblicazione: (2024)

Studying and Benchmarking Large Language Models For Log Level Suggestion
di: Heng, Yi Wen, et al.
Pubblicazione: (2024)

Logging Requirement for Continuous Auditing of Responsible Machine Learning-based Applications
di: Foalem, Patrick Loic, et al.
Pubblicazione: (2025)

LogReasoner: Empowering LLMs with Expert-like Coarse-to-Fine Reasoning for Automated Log Analysis
di: Ma, Lipeng, et al.
Pubblicazione: (2025)

LogUpdater: Automated Detection and Repair of Specific Defects in Logging Statements
di: Zhong, Renyi, et al.
Pubblicazione: (2024)

How Do Developers Use Migration Guides? A Case Study of Log4j
di: Monno, Takahiro, et al.
Pubblicazione: (2026)

Agent READMEs: An Empirical Study of Context Files for Agentic Coding
di: Chatlatanagulchai, Worawalan, et al.
Pubblicazione: (2025)

Generating High-Level Test Cases from Requirements using LLM: An Industry Study
di: Masuda, Satoshi, et al.
Pubblicazione: (2025)

Do AI Coding Agents Log Like Humans? An Empirical Study
di: Ouatiti, Youssef Esseddiq, et al.
Pubblicazione: (2026)

Scalable and Efficient Large-Scale Log Analysis with LLMs: An IT Software Support Case Study
di: Gupta, Pranjal, et al.
Pubblicazione: (2025)

Logging Like Humans for LLMs: Rethinking Logging via Execution and Runtime Feedback
di: Wang, Xin, et al.
Pubblicazione: (2026)

Mining for Lags in Updating Critical Security Threats: A Case Study of Log4j Library
di: Tanaka, Hidetake, et al.
Pubblicazione: (2025)