Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification | Paper Citation Record

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This paper cites Methods for interpreting and understanding deep neural networks,.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Methods for interpreting and understanding deep neural networks,

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Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Deep learning approach for intelligent financial fraud detection system,

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This paper cites Explaining software bugs leveraging code structures in neural machine translation,.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Explaining software bugs leveraging code structures in neural machine translation,

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This paper cites Predicting line-level defects by capturing code contexts with hierarchical transformers,.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Predicting line-level defects by capturing code contexts with hierarchical transformers,

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This paper cites A guide to deep learning in healthcare,.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification A guide to deep learning in healthcare,

Reference 5

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This paper cites Sphereface: Deep hypersphere embedding for face recogni- tion,.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Sphereface: Deep hypersphere embedding for face recogni- tion,

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Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification A survey of deep learning techniques for autonomous driving,

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This paper cites Understanding software- 2.0: A study of machine learning library usage and evolution,.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Understanding software- 2.0: A study of machine learning library usage and evolution,

Reference 8

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This paper cites Deep Learning & Software Engineering: State of Research and Future Directions.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Deep Learning & Software Engineering: State of Research and Future Directions

Reference 9

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This paper cites Towards Understanding the Challenges of Bug Localization in Deep Learning Systems.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Towards Understanding the Challenges of Bug Localization in Deep Learning Systems

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This paper cites Jaguar: A spectrum-based fault localization tool for real-world software,.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Jaguar: A spectrum-based fault localization tool for real-world software,

Reference 11

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This paper cites Mahbub, Comprehending software bugs leveraging code structures with neural language models.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Mahbub, Comprehending software bugs leveraging code structures with neural language models

Reference 12

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This paper cites Artificial neural networks based optimization techniques: A review,.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Artificial neural networks based optimization techniques: A review,

Reference 13

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Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification An empirical study on tensorflow program bugs,

Reference 14

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Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification A comprehen- sive study on deep learning bug characteristics,

Reference 15

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This paper cites Taxonomy of real faults in deep learning systems,.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Taxonomy of real faults in deep learning systems,

Reference 16

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Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Testing feedforward neural networks training programs,

Reference 17

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This paper cites Bugs in machine learning-based systems: A faultload bench- mark,.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Bugs in machine learning-based systems: A faultload bench- mark,

Reference 18

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This paper cites Toward un- derstanding deep learning framework bugs,.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Toward un- derstanding deep learning framework bugs,

Reference 19

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Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Detecting numerical bugs in neural network archi- tectures,

Reference 20

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Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Deeplocalize: Fault lo- calization for deep neural networks,

Reference 21

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Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Automatic fault detection for deep learning programs using graph transformations,

Reference 22

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Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Umlaut: Debugging deep learning programs using program structure and model behavior,

Reference 23

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Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Autotrainer: An automatic dnn training problem detection and repair system,

Reference 24

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Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Deepdiagnosis: Automatically diagnosing faults and recommending actionable fixes in deep learning programs,

Reference 25

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Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Deepcrime: Mutation testing of deep learning systems based on real faults,

Reference 26

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Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Deepfd: Automated fault diagnosis and localization for deep learning programs,

Reference 27

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Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Cradle: Cross- backend validation to detect and localize bugs in deep learning libraries,

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Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Deepmutation: Mutation testing of deep learning systems,

Reference 29

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This paper cites science/r/ICSE 2025-F42C, Accessed: 2024-11-29, 2025.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification science/r/ICSE 2025-F42C, Accessed: 2024-11-29, 2025

Reference 30

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Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Nerdbug: Automated bug detection in neural networks,

Reference 31

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Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Tensfa: Detecting and repairing tensor shape faults in deep learning systems,

Reference 32

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Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Mode: Automated neural network model debugging via state dif- ferential analysis and input selection,

Reference 33

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Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Exposing numerical bugs in deep learning via gradient back- propagation,

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Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Mutation-based fault localization of deep neural networks,

Reference 35

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This paper cites Deepfault: Fault localization for deep neural networks,.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Deepfault: Fault localization for deep neural networks,

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Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification An Effective Data-Driven Approach for Localizing Deep Learning Faults

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This paper cites Towards enhanc- ing the reproducibility of deep learning bugs: An empirical study,.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Towards enhanc- ing the reproducibility of deep learning bugs: An empirical study,

Reference 38

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Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Meta-analysis of cohen’s kappa,

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This paper cites Goodfellow, Y.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Goodfellow, Y

Reference 40

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This paper cites Johnson, Convolutional Neural Networks for Visual Recog- nition.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Johnson, Convolutional Neural Networks for Visual Recog- nition

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This paper cites Understanding the effect size and its measures.,.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Understanding the effect size and its measures.,

Reference 42

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Observation 3d7a81a0-69ab-4ef9-b19b-62e26beef020 · outbound

This paper cites Alternatives to p value: Confidence interval and effect size,.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Alternatives to p value: Confidence interval and effect size,

Reference 43

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This paper cites Deep- mutation++: A mutation testing framework for deep learning systems,.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Deep- mutation++: A mutation testing framework for deep learning systems,

Reference 44

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Observation bbb37caa-c133-45fa-bdbb-e3b9f019cf58 · outbound

This paper cites How to kill them all: An exploratory study on the impact of code observability on mutation testing,.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification How to kill them all: An exploratory study on the impact of code observability on mutation testing,

Reference 45

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Observation 5b68017c-fdcd-4eab-ac50-f5c78ff82435 · outbound

This paper cites Automatic mlp weight regularization on min- eralization prediction tasks,.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Automatic mlp weight regularization on min- eralization prediction tasks,

Reference 46

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Observation b8671818-6c8c-4a60-98aa-88f352060634 · outbound

This paper cites High- dimensional dynamics of generalization error in neural net- works,.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification High- dimensional dynamics of generalization error in neural net- works,

Reference 47

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Observation 82628dcc-48fe-46f5-999c-8ede3d7ec674 · outbound

This paper cites Deepsniffer: A dnn model extraction framework based on learning architectural hints,.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Deepsniffer: A dnn model extraction framework based on learning architectural hints,

Reference 48

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Observation 0d967a9d-5070-4f02-ba88-a1eece5ffed7 · outbound

This paper cites A comprehensive study of deep learning compiler bugs,.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification A comprehensive study of deep learning compiler bugs,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:07:53.037165Z

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Observation f65bb0db-fb98-4280-9a2d-eb41b8d36466 · outbound

This paper cites Understanding deep learning (still) requires rethinking gen- eralization,.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Understanding deep learning (still) requires rethinking gen- eralization,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:07:53.023907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d4644ac4-e6af-47ab-a517-51b19b2da4c9 · outbound

This paper cites Understanding neural networks via feature visualization: A survey,.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Understanding neural networks via feature visualization: A survey,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:07:53.010989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c3e98637-91ad-4714-a70c-19e26d0cb51b · outbound

This paper cites Practical recommendations for gradient-based training of deep architectures,.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Practical recommendations for gradient-based training of deep architectures,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:07:52.997891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0900d2fa-e93e-4f4d-aa5d-0dc2704fe85b · outbound

This paper cites Cyclical learning rates for training neural networks,.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Cyclical learning rates for training neural networks,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:07:52.984052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 55f332e5-5edc-4f2b-be42-37fee6bbc5be · outbound

This paper cites A high- throughput screening approach to discovering good forms of biologically inspired visual representation,.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification A high- throughput screening approach to discovering good forms of biologically inspired visual representation,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:07:52.970799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation db5d9924-8efa-4e3f-ac25-5e0bd2788a8b · outbound

This paper cites Understanding the difficulty of training deep feedforward neural networks,.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Understanding the difficulty of training deep feedforward neural networks,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:07:52.957504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0f8caad2-9de5-48da-a523-147bc54f069f · outbound

This paper cites Hierar- chical classification,.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Hierar- chical classification,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:07:52.944876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation db33b6d9-d980-42c9-955b-6118341983ab · outbound

This paper cites Random forest,.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Random forest,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:07:52.932511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 649e9b04-0edd-4aa3-8224-307d8a012871 · outbound

This paper cites Random forests,.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Random forests,

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-10T17:07:52.420755Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07fe279d-d9b5-4913-9f80-1ae593bc1ffb · outbound

This paper cites Performance comparison of grid search and random search methods for hyperparameter tuning in extreme gradient boosting algorithm to predict chronic kidney failure.,.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Performance comparison of grid search and random search methods for hyperparameter tuning in extreme gradient boosting algorithm to predict chronic kidney failure.,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:07:52.913307Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 061a8b30-2a02-48d3-8728-97d16fba0576 · outbound

This paper cites Random search for hyper- parameter optimization.,.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Random search for hyper- parameter optimization.,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:07:52.901393Z

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Observation b0d083f2-c3a5-4292-a89e-cb66825dfda2 · outbound

This paper cites Explainable artificial intelligence: An analytical review,.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Explainable artificial intelligence: An analytical review,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:07:52.889109Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2c5442f6-7118-4c68-ab21-e272a889d3f2 · outbound

This paper cites Failure mode and effects analysis of rc members based on machine-learning- based shapley additive explanations (shap) approach,.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Failure mode and effects analysis of rc members based on machine-learning- based shapley additive explanations (shap) approach,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:07:52.877275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 42d23251-989b-4682-bfdf-e5d94cd93138 · outbound

This paper cites Cross-validation metrics for evaluating classification performance on imbalanced data,.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Cross-validation metrics for evaluating classification performance on imbalanced data,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:07:52.865728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6e104342-2635-4e69-8935-e08f7bf3ec43 · outbound

This paper cites Layer-wise learning based stochastic gradient descent method for the optimization of deep convolutional neural network,.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Layer-wise learning based stochastic gradient descent method for the optimization of deep convolutional neural network,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:07:52.854391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3a68309a-cd23-4705-b1e7-116855214ac2 · outbound

This paper cites Reltanh: An activation function with vanishing gradient resistance for sae-based dnns and its application to rotating machinery fault diagnosis,.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Reltanh: An activation function with vanishing gradient resistance for sae-based dnns and its application to rotating machinery fault diagnosis,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:07:52.842620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d7767484-8d30-42e6-a1d1-145efaf5415c · outbound

This paper cites an unresolved cited work.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Unresolved cited work

Reference 66

Resolution: unresolved; raw_fallback, observed 2026-08-10T17:07:52.830648Z

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Observation ff0abfb1-7f15-44ae-8c08-c6ea13f31014 · outbound

This paper cites From local explanations to global understanding with explainable ai for trees,.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification From local explanations to global understanding with explainable ai for trees,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:07:52.818852Z

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Observation 08e2af1c-cef1-4d35-b9fe-8ebf44e831b8 · outbound

This paper cites A unified optimization approach for sparse tensor operations on gpus,.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification A unified optimization approach for sparse tensor operations on gpus,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:07:52.806701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d7418bcb-1c44-42d4-9722-5a817ce2c485 · outbound

This paper cites The general inefficiency of batch training for gradient descent learning,.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification The general inefficiency of batch training for gradient descent learning,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:07:52.795542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 71362ed2-b66f-4e7b-863c-0c6edb65b9da · outbound

This paper cites On locality of local explanation models,.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification On locality of local explanation models,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:07:52.784147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 49db1c41-5a1b-4ff7-926f-714ad4f3df3c · outbound

This paper cites Technologies, Tensorflow 2 published models , https : / / github.com/sarus-tech/tf2-published-models, Accessed: 2024- 11-21, 2023.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Technologies, Tensorflow 2 published models , https : / / github.com/sarus-tech/tf2-published-models, Accessed: 2024- 11-21, 2023

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:07:52.772507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8efb85a6-c53c-4036-84c8-febfc9560424 · outbound

This paper cites Gradient- based learning applied to document recognition,.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Gradient- based learning applied to document recognition,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:07:52.760648Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 46b7a947-7a77-43bc-84ef-a1c0e6fa32a0 · outbound

This paper cites Cnn model for image classification on mnist and fashion-mnist dataset,.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification Cnn model for image classification on mnist and fashion-mnist dataset,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:07:52.749041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f938905e-2be2-48cf-a6fe-a3a766077bad · outbound

This paper cites An analysis of the softmax cross entropy loss for learning-to- rank with binary relevance,.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification An analysis of the softmax cross entropy loss for learning-to- rank with binary relevance,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:07:52.735943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f582e033-2500-4fd6-9da4-715080a71e3c · outbound

This paper cites A hitchhiker’s guide to statis- tical tests for assessing randomized algorithms in software engineering,.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification A hitchhiker’s guide to statis- tical tests for assessing randomized algorithms in software engineering,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:07:52.722173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9bf0a1e7-1966-4e20-8abe-a568b82cf49c · outbound

This paper cites On construct validity: Issues of method and measurement.,.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification On construct validity: Issues of method and measurement.,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:07:52.709039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b65c1ad2-5447-40c4-abaf-b068bbc09b07 · outbound

This paper cites External validity,.

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification External validity,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:07:52.692443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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