Image de couverture de livre

Inferring Pluggable Types with Machine Learning

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Siddiqui, Kazi Amanul Islam, Kellogg, Martin
Format:	Preprint
Publié:	2024
Sujets:	Software Engineering Artificial Intelligence
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Inferring Code Correctness from Specification
par: Florian, Tambon, et autres
Publié: (2026)

Breaking the Myth: Can Small Models Infer Postconditions Too?
par: Zhang, Gehao, et autres
Publié: (2025)

The Machine Learning Canvas: Empirical Findings on Why Strategy Matters More Than AI Code Generation
par: Prause, Martin
Publié: (2026)

CodeCircuit: Toward Inferring LLM-Generated Code Correctness via Attribution Graphs
par: He, Yicheng, et autres
Publié: (2026)

Quality Model for Machine Learning Components
par: Lewis, Grace A., et autres
Publié: (2026)

Naming the Pain in Machine Learning-Enabled Systems Engineering
par: Kalinowski, Marcos, et autres
Publié: (2024)

Explaining the Contributing Factors for Vulnerability Detection in Machine Learning
par: Mouine, Esma, et autres
Publié: (2024)

An Empirical Study of Challenges in Machine Learning Asset Management
par: Zhao, Zhimin, et autres
Publié: (2024)

The Role of Artificial Intelligence and Machine Learning in Software Testing
par: Ramadan, Ahmed, et autres
Publié: (2024)

Automatic Identification of Machine Learning-Specific Code Smells
par: Hamfelt, Peter, et autres
Publié: (2025)

Machine Learning Techniques for Python Source Code Vulnerability Detection
par: Farasat, Talaya, et autres
Publié: (2024)

Agile Management for Machine Learning: A Systematic Mapping Study
par: Romao, Lucas, et autres
Publié: (2025)

Define-ML: An Approach to Ideate Machine Learning-Enabled Systems
par: Alonso, Silvio, et autres
Publié: (2025)

RAILS: Retrieval-Augmented Intelligence for Learning Software Development
par: Abdullah, Wali Mohammad, et autres
Publié: (2025)

FlaKat: A Machine Learning-Based Categorization Framework for Flaky Tests
par: Lin, Shizhe, et autres
Publié: (2024)

When the Code Autopilot Breaks: Why LLMs Falter in Embedded Machine Learning
par: Morabito, Roberto, et autres
Publié: (2025)

Studying the Impact of TensorFlow and PyTorch Bindings on Machine Learning Software Quality
par: Li, Hao, et autres
Publié: (2024)

Human-Machine Co-Boosted Bug Report Identification with Mutualistic Neural Active Learning
par: Long, Guoming, et autres
Publié: (2026)

Quantifying Uncertainty in Machine Learning-Based Pervasive Systems: Application to Human Activity Recognition
par: Balditsyn, Vladimir, et autres
Publié: (2025)

A Systematic Literature Review on Explainability for Machine/Deep Learning-based Software Engineering Research
par: Cao, Sicong, et autres
Publié: (2024)

Towards Fair Machine Learning Software: Understanding and Addressing Model Bias Through Counterfactual Thinking
par: Wang, Zichong, et autres
Publié: (2023)

LLM-Powered Code Vulnerability Repair with Reinforcement Learning and Semantic Reward
par: Islam, Nafis Tanveer, et autres
Publié: (2024)

Machine Learning Experiences: A story of learning AI for use in enterprise software testing that can be used by anyone
par: Cohoon, Michael, et autres
Publié: (2025)

CodeClash: Benchmarking Goal-Oriented Software Engineering
par: Yang, John, et autres
Publié: (2025)

CATCODER: Repository-Level Code Generation with Relevant Code and Type Context
par: Pan, Zhiyuan, et autres
Publié: (2024)

AutoSafeCoder: A Multi-Agent Framework for Securing LLM Code Generation through Static Analysis and Fuzz Testing
par: Nunez, Ana, et autres
Publié: (2024)

Safer Builders, Risky Maintainers: A Comparative Study of Breaking Changes in Human vs Agentic PRs
par: Ferdous, K M, et autres
Publié: (2026)

Identifying Technical Debt and Its Types Across Diverse Software Projects Issues
par: Shivashankar, Karthik, et autres
Publié: (2024)

Learning to Guarantee Type Correctness in Code Generation through Type-Guided Program Synthesis
par: Huang, Zhechong, et autres
Publié: (2025)

Teaching Machines to Code: Smart Contract Translation with LLMs
par: Karanjai, Rabimba, et autres
Publié: (2024)

Refactoring with LLMs: Bridging Human Expertise and Machine Understanding
par: Piao, Yonnel Chen Kuang, et autres
Publié: (2025)

To Be Forgotten or To Be Fair: Unveiling Fairness Implications of Machine Unlearning Methods
par: Zhang, Dawen, et autres
Publié: (2023)

Invisible Load: Uncovering the Challenges of Neurodivergent Women in Software Engineering
par: Zaib, Munazza, et autres
Publié: (2025)

A-ProS: Towards Reliable Autonomous Programming Through Multi-Model Feedback
par: Tabassum, Anika, et autres
Publié: (2026)

PyGen: A Collaborative Human-AI Approach to Python Package Creation
par: Barua, Saikat, et autres
Publié: (2024)

Machine Learning Robustness: A Primer
par: Braiek, Houssem Ben, et autres
Publié: (2024)

Code for Machines, Not Just Humans: Quantifying AI-Friendliness with Code Health Metrics
par: Borg, Markus, et autres
Publié: (2026)

Machine Learning with Requirements: a Manifesto
par: Giunchiglia, Eleonora, et autres
Publié: (2023)

Code Security Vulnerability Repair Using Reinforcement Learning with Large Language Models
par: Islam, Nafis Tanveer, et autres
Publié: (2024)

Harnessing the Power of LLMs: Automating Unit Test Generation for High-Performance Computing
par: Karanjai, Rabimba, et autres
Publié: (2024)