Cover Image

MIST-RL: Mutation-based Incremental Suite Testing via Reinforcement Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhu, Sicheng, Wang, Jiajun, Ai, Jiawei, Li, Xin
Format:	Preprint
Published:	2026
Subjects:	Artificial Intelligence Machine Learning Software Engineering
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Mutation-Guided LLM-based Test Generation at Meta
by: Foster, Christopher, et al.
Published: (2025)

VeriScale: Adversarial Test-Suite Scaling for Verifiable Code Generation
by: Bai, Yifan, et al.
Published: (2026)

Assuring the Safety of Reinforcement Learning Components: AMLAS-RL
by: Imrie, Calum Corrie, et al.
Published: (2025)

Exploring Pass-Rate Reward in Reinforcement Learning for Code Generation
by: Li, Xin-Ye, et al.
Published: (2026)

Beyond Verifiable Rewards: Rubric-Based GRM for Reinforced Fine-Tuning SWE Agents
by: Huang, Jiawei, et al.
Published: (2026)

PIPer: On-Device Environment Setup via Online Reinforcement Learning
by: Kovrigin, Alexander, et al.
Published: (2025)

Automatic Generation of High-Performance RL Environments
by: Karten, Seth, et al.
Published: (2026)

Reinforcement Learning for Online Testing of Autonomous Driving Systems: a Replication and Extension Study
by: Giamattei, Luca, et al.
Published: (2024)

MILE: A Mutation Testing Framework of In-Context Learning Systems
by: Wei, Zeming, et al.
Published: (2024)

A Reference Architecture of Reinforcement Learning Frameworks
by: Liu, Xiaoran, et al.
Published: (2026)

MASTEST: A LLM-Based Multi-Agent System For RESTful API Tests
by: Han, Xiaoke, et al.
Published: (2025)

RBT4DNN: Requirements-based Testing of Neural Networks
by: Mozumder, Nusrat Jahan, et al.
Published: (2025)

ADReFT: Adaptive Decision Repair for Safe Autonomous Driving via Reinforcement Fine-Tuning
by: Cheng, Mingfei, et al.
Published: (2025)

DeepKnowledge: Generalisation-Driven Deep Learning Testing
by: Missaoui, Sondess, et al.
Published: (2024)

Keeping Code-Aware LLMs Fresh: Full Refresh, In-Context Deltas, and Incremental Fine-Tuning
by: Sharma, Pradeep Kumar, et al.
Published: (2025)

Toward Debugging Deep Reinforcement Learning Programs with RLExplorer
by: Bouchoucha, Rached, et al.
Published: (2024)

SWE-MiniSandbox: Container-Free Reinforcement Learning for Building Software Engineering Agents
by: Yuan, Danlong, et al.
Published: (2026)

The Impact of Software Testing with Quantum Optimization Meets Machine Learning
by: Bandarupalli, Gopichand
Published: (2025)

Complex Model Transformations by Reinforcement Learning with Uncertain Human Guidance
by: Dagenais, Kyanna, et al.
Published: (2025)

Model Provenance via Model DNA
by: Mu, Xin, et al.
Published: (2023)

Code Generation by Differential Test Time Scaling
by: He, Yifeng, et al.
Published: (2026)

SMARLA: A Safety Monitoring Approach for Deep Reinforcement Learning Agents
by: Zolfagharian, Amirhossein, et al.
Published: (2023)

BatCoder: Self-Supervised Bidirectional Code-Documentation Learning via Back-Translation
by: Xu, Jingwen, et al.
Published: (2026)

LGMT: Logic-Grounded Metamorphic Testing for Evaluating the Reasoning Reliability of LLMs
by: Zhou, Zenghui, et al.
Published: (2026)

Testing of Deep Reinforcement Learning Agents with Surrogate Models
by: Biagiola, Matteo, et al.
Published: (2023)

Kevin: Multi-Turn RL for Generating CUDA Kernels
by: Baronio, Carlo, et al.
Published: (2025)

Unlock the Correlation between Supervised Fine-Tuning and Reinforcement Learning in Training Code Large Language Models
by: Chen, Jie, et al.
Published: (2024)

Parameter-Efficient Fine-Tuning of Large Language Models for Unit Test Generation: An Empirical Study
by: Storhaug, André, et al.
Published: (2024)

Can Search-Based Testing with Pareto Optimization Effectively Cover Failure-Revealing Test Inputs?
by: Sorokin, Lev, et al.
Published: (2024)

Cross-System Categorization of Abnormal Traces in Microservice-Based Systems via Meta-Learning
by: Wang, Yuqing, et al.
Published: (2024)

Sketch-and-Verify: Structured Inference-Time Scaling via Program Sketching
by: Jiang, Shan, et al.
Published: (2026)

FlakyFix: Using Large Language Models for Predicting Flaky Test Fix Categories and Test Code Repair
by: Fatima, Sakina, et al.
Published: (2023)

Co-Located Tests, Better AI Code: How Test Syntax Structure Affects Foundation Model Code Generation
by: Jacopin, Éric
Published: (2026)

On the Replicability and Reproducibility of Deep Learning in Software Engineering
by: Liu, Chao, et al.
Published: (2020)

Understanding LLM-Driven Test Oracle Generation
by: Bodicoat, Adam, et al.
Published: (2026)

Generative AI to Generate Test Data Generators
by: Baudry, Benoit, et al.
Published: (2024)

Mutation-based Consistency Testing for Evaluating the Code Understanding Capability of LLMs
by: Li, Ziyu, et al.
Published: (2024)

A Theoretical Analysis of Test-Driven Code Generation
by: Menet, Nicolas, et al.
Published: (2026)

Bridging Online and Offline RL: Contextual Bandit Learning for Multi-Turn Code Generation
by: Chen, Ziru, et al.
Published: (2026)

LLM-based Content Classification Approach for GitHub Repositories by the README Files
by: Mehmood, Malik Uzair, et al.
Published: (2025)