Nargiz Humbatova

Orcid: 0000-0002-3037-8368

According to our database1, Nargiz Humbatova authored at least 21 papers between 2020 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

Online presence:

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Bibliography

2024
Spectral Analysis of the Relation between Deep Learning Faults and Neural Activation Values: Replication Package.
Dataset, January, 2024

muPRL: A Mutation Testing Pipeline for Deep Reinforcement Learning based on Real Faults.
CoRR, 2024

Spectral Analysis of the Relation between Deep Learning Faults and Neural Activation Values.
Proceedings of the IEEE Conference on Software Testing, Verification and Validation, 2024

2023
Repairing DNN Architecture: Are We There Yet?
Proceedings of the IEEE Conference on Software Testing, Verification and Validation, 2023

DeepCrime: from Real Faults to Mutation Testing Tool for Deep Learning.
Proceedings of the 45th IEEE/ACM International Conference on Software Engineering: ICSE 2023 Companion Proceedings, 2023

2021
Experimental data for "DeepMetis: Augmenting a Deep Learning Test Set to Increase its Mutation Score" paper.
Dataset, July, 2021

DeepCrime: Mutation Testing of Deep Learning Systems Based on Real Faults (Tool).
Dataset, July, 2021

Replication package for the "DeepCrime: Mutation Testing of Deep Learning Systems based on Real Faults" paper.
Dataset, May, 2021

Replication package for the "DeepCrime: Mutation Testing of Deep Learning Systems based on Real Faults" paper.
Dataset, May, 2021

DeepCrime and DeepMutation++ mutations for Speaker Recognition system.
Dataset, May, 2021

DeepCrime mutations for Speaker Recognition system.
Dataset, May, 2021

DeepCrime and DeepMutation++ mutations for Udacity self-driving car system.
Dataset, May, 2021

DeepCrime mutations for Udacity self-driving car system.
Dataset, May, 2021

DeepCrime and DeepMutation++ mutations for MNIST.
Dataset, May, 2021

DeepCrime mutations for MNIST.
Dataset, May, 2021

DeepCrime and DeepMutation++ mutations for UnityEyes and Movie Recommender Systems.
Dataset, May, 2021

DeepMetis: Augmenting a Deep Learning Test Set to Increase its Mutation Score.
Proceedings of the 36th IEEE/ACM International Conference on Automated Software Engineering, 2021

DeepCrime: mutation testing of deep learning systems based on real faults.
Proceedings of the ISSTA '21: 30th ACM SIGSOFT International Symposium on Software Testing and Analysis, 2021

2020
Dataset of Real Faults in Deep Learning Systems.
Dataset, February, 2020

Testing machine learning based systems: a systematic mapping.
Empir. Softw. Eng., 2020

Taxonomy of real faults in deep learning systems.
Proceedings of the ICSE '20: 42nd International Conference on Software Engineering, Seoul, South Korea, 27 June, 2020


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