Cover Image

Detecting and Fixing API Misuses of Data Science Libraries Using Large Language Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Galappaththi, Akalanka, Ribeiro, Francisco, Nadi, Sarah
Format:	Preprint
Published:	2025
Subjects:	Software Engineering
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

An Empirical Study of API Misuses of Data-Centric Libraries
by: Galappaththi, Akalanka, et al.
Published: (2024)

Identifying and Mitigating API Misuse in Large Language Models
by: Zhuo, Terry Yue, et al.
Published: (2025)

Byam: Fixing Breaking Dependency Updates with Large Language Models
by: Reyes, Frank, et al.
Published: (2025)

Generating API Parameter Security Rules with LLM for API Misuse Detection
by: Liu, Jinghua, et al.
Published: (2024)

An Empirical Study of Python Library Migration Using Large Language Models
by: Islam, Md Mohayeminul, et al.
Published: (2025)

AFGNN: API Misuse Detection using Graph Neural Networks and Clustering
by: Pirapuraj, Ponnampalam, et al.
Published: (2026)

Can We Trust the AI Pair Programmer? Copilot for API Misuse Detection and Correction
by: Mondal, Saikat, et al.
Published: (2025)

Your Fix Is My Exploit: Enabling Comprehensive DL Library API Fuzzing with Large Language Models
by: Zhang, Kunpeng, et al.
Published: (2025)

Mutation-based Evaluation of Cryptographic API Misuse Detectors
by: Ami, Amit Seal, et al.
Published: (2021)

Automated Program Repair Based on REST API Specifications Using Large Language Models
by: Yamagishi, Katsuki, et al.
Published: (2025)

Characterizing Python Library Migrations
by: Islam, Mohayeminul, et al.
Published: (2022)

Leveraging Large Language Models to Improve REST API Testing
by: Kim, Myeongsoo, et al.
Published: (2023)

MigMate: A VS Code Extension for LLM-based Library Migration of Python Projects
by: Kebede, Matthias, et al.
Published: (2026)

KAT: Dependency-aware Automated API Testing with Large Language Models
by: Le, Tri, et al.
Published: (2024)

Automated Update of Android Deprecated API Usages with Large Language Models
by: Mahmud, Tarek, et al.
Published: (2024)

On LLMs' Internal Representation of Code Correctness
by: Ribeiro, Francisco, et al.
Published: (2025)

Evaluating Cryptographic API Misuse Detectors for Go
by: Andersson, Vivi, et al.
Published: (2026)

Evaluating the Effectiveness of LLMs in Fixing Maintainability Issues in Real-World Projects
by: Nunes, Henrique, et al.
Published: (2025)

Systematic API Testing Through Model Checking and Executable Contracts
by: Ribeiro, Ana, et al.
Published: (2026)

How the Misuse of a Dataset Harmed Semantic Clone Detection
by: Krinke, Jens, et al.
Published: (2025)

OASBuilder: Generating OpenAPI Specifications from Online API Documentation with Large Language Models
by: Lazar, Koren, et al.
Published: (2025)

Satellite: Detecting and Analyzing Smart Contract Vulnerabilities caused by Subcontract Misuse
by: Liao, Zeqin, et al.
Published: (2025)

APIDocBooster: An Extract-Then-Abstract Framework Leveraging Large Language Models for Augmenting API Documentation
by: Yang, Chengran, et al.
Published: (2023)

MLmisFinder: A Specification and Detection Approach of Machine Learning Service Misuses
by: Amor, Hadil Ben, et al.
Published: (2026)

Evaluating Large Language Models in Detecting Test Smells
by: Lucas, Keila, et al.
Published: (2024)

HAFix: History-Augmented Large Language Models for Bug Fixing
by: Shi, Yu, et al.
Published: (2025)

LibScan: Smart Contract Library Misuse Detection with Iterative Feedback and Static Verification
by: Wang, Yishun, et al.
Published: (2026)

Detecting and Mitigating Flakiness in REST API Fuzzing
by: Zhang, Man, et al.
Published: (2026)

Detecting Misuse of Security APIs: A Systematic Review
by: Mousavi, Zahra, et al.
Published: (2023)

R+R: Reassessing Java Security API Misuse in Current LLMs: A Replication on JCA and JSSE APIs with External Security Knowledge
by: Lu, Tianhe, et al.
Published: (2026)

Fixing Large Language Models' Specification Misunderstanding for Better Code Generation
by: Tian, Zhao, et al.
Published: (2023)

MigrateLib: a tool for end-to-end Python library migration
by: Islam, Mohayeminul, et al.
Published: (2025)

Enhancing the Capabilities of Large Language Models for API calls through Knowledge Graphs
by: Yang, Ye, et al.
Published: (2025)

Client--Library Compatibility Testing with API Interaction Snapshots
by: Monce, Gustave, et al.
Published: (2025)

Fixing Function-Level Code Generation Errors for Foundation Large Language Models
by: Wen, Hao, et al.
Published: (2024)

Contextual API Completion for Unseen Repositories Using LLMs
by: Nashid, Noor, et al.
Published: (2024)

Automatic Library Migration Using Large Language Models: First Results
by: Almeida, Aylton, et al.
Published: (2024)

Detecting Multi-Parameter Constraint Inconsistencies in Python Data Science Libraries
by: Xu, Xiufeng, et al.
Published: (2024)

A Systematic Evaluation of Large Code Models in API Suggestion: When, Which, and How
by: Wang, Chaozheng, et al.
Published: (2024)

ToolMisuseBench: An Offline Deterministic Benchmark for Tool Misuse and Recovery in Agentic Systems
by: Sigdel, Akshey, et al.
Published: (2026)