Cover Image

Real Faults in Deep Learning Fault Benchmarks: How Real Are They?

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Jahangirova, Gunel, Humbatova, Nargiz, Kim, Jinhan, Yoo, Shin, Tonella, Paolo
Format:	Preprint
Published:	2024
Subjects:	Software Engineering Artificial Intelligence Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

An Empirical Study of Fault Localisation Techniques for Deep Learning
by: Humbatova, Nargiz, et al.
Published: (2024)

Fault Localisation and Repair for DL Systems: An Empirical Study with LLMs
by: Kim, Jinhan, et al.
Published: (2025)

MuFF: Stable and Sensitive Post-training Mutation Testing for Deep Learning
by: Kim, Jinhan, et al.
Published: (2025)

New Formulation of DNN Statistical Mutation Killing for Ensuring Monotonicity: A Technical Report
by: Kim, Jinhan, et al.
Published: (2025)

Revisiting "Revisiting Neuron Coverage for DNN Testing: A Layer-Wise and Distribution-Aware Criterion": A Critical Review and Implications on DNN Coverage Testing
by: Kim, Jinhan, et al.
Published: (2026)

muPRL: A Mutation Testing Pipeline for Deep Reinforcement Learning based on Real Faults
by: Thomas, Deepak-George, et al.
Published: (2024)

TopoMap: A Feature-based Semantic Discriminator of the Topographical Regions in the Test Input Space
by: De Vita, Gianmarco, et al.
Published: (2025)

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

A Taxonomy of Real Faults in Hybrid Quantum-Classical Architectures
by: Bensoussan, Avner, et al.
Published: (2025)

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

Detecting Trojaned DNNs via Spectral Regression Analysis
by: Pasini, Samuele, et al.
Published: (2026)

Cross-site scripting adversarial attacks based on deep reinforcement learning: Evaluation and extension study
by: Pasini, Samuele, et al.
Published: (2025)

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

GenMorph: Automatically Generating Metamorphic Relations via Genetic Programming
by: Ayerdi, Jon, et al.
Published: (2023)

SETA: Statistical Fault Attribution for Compound AI Systems
by: Chowdhury, Sayak, et al.
Published: (2026)

Order Matters! An Empirical Study on Large Language Models' Input Order Bias in Software Fault Localization
by: Rafi, Md Nakhla, et al.
Published: (2024)

Assessing the Impact of Code Changes on the Fault Localizability of Large Language Models
by: Haroon, Sabaat, et al.
Published: (2025)

COSMosFL: Ensemble of Small Language Models for Fault Localisation
by: Cho, Hyunjoon, et al.
Published: (2025)

MobiFlow: Real-World Mobile Agent Benchmarking through Trajectory Fusion
by: Feng, Yunfei, et al.
Published: (2026)

Deep-Bench: Deep Learning Benchmark Dataset for Code Generation
by: Daghighfarsoodeh, Alireza, et al.
Published: (2025)

FetaFix: Automatic Fault Localization and Repair of Deep Learning Model Conversions
by: Louloudakis, Nikolaos, et al.
Published: (2023)

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

DiTOX: Fault Detection and Localization in the ONNX Optimizer
by: Louloudakis, Nikolaos, et al.
Published: (2025)

High-Dimensional Fault Tolerance Testing of Highly Automated Vehicles Based on Low-Rank Models
by: Mei, Yuewen, et al.
Published: (2024)

Boundary State Generation for Testing and Improvement of Autonomous Driving Systems
by: Biagiola, Matteo, et al.
Published: (2023)

Evaluating and Improving the Robustness of Security Attack Detectors Generated by LLMs
by: Pasini, Samuele, et al.
Published: (2024)

Efficient Domain Augmentation for Autonomous Driving Testing Using Diffusion Models
by: Baresi, Luciano, et al.
Published: (2024)

DANDI: Diffusion as Normative Distribution for Deep Neural Network Input
by: Kim, Somin, et al.
Published: (2025)

SWT-Bench: Testing and Validating Real-World Bug-Fixes with Code Agents
by: Mündler, Niels, et al.
Published: (2024)

How Efficient is LLM-Generated Code? A Rigorous & High-Standard Benchmark
by: Qiu, Ruizhong, et al.
Published: (2024)

Deploying Geospatial Foundation Models in the Real World: Lessons from WorldCereal
by: Butsko, Christina, et al.
Published: (2025)

Investigating Reproducibility in Deep Learning-Based Software Fault Prediction
by: Mukhtar, Adil, et al.
Published: (2024)

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

Enhancing Resilience and Scalability in Travel Booking Systems: A Microservices Approach to Fault Tolerance, Load Balancing, and Service Discovery
by: Barua, Biman, et al.
Published: (2024)

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

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

LLMSYS-HPOBench: Hyperparameter Optimization Benchmark Suite for Real-World LLM Systems
by: Wu, Siyu, et al.
Published: (2026)

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

Fault Localization in Deep Learning-based Software: A System-level Approach
by: Morovati, Mohammad Mehdi, et al.
Published: (2024)

The Fault in our Stars: Quality Assessment of Code Generation Benchmarks
by: Siddiq, Mohammed Latif, et al.
Published: (2024)