1
BUGSJS: a benchmark and taxonomy of JavaScript bugs
2
An Empirical Evaluation of Mutation Operators for Deep Learning Systems
3
Model-based exploration of the frontier of behaviours for deep learning system testing
4
Benefitting from the Grey Literature in Software Engineering Research
5
Taxonomy of Real Faults in Deep Learning Systems
6
Misbehaviour Prediction for Autonomous Driving Systems
7
DeepStellar: model-based quantitative analysis of stateful deep learning systems
8
A Systematic Mapping Study on Testing of Machine Learning Programs
9
DeepHunter: a coverage-guided fuzz testing framework for deep neural networks
10
Machine Learning Testing: Survey, Landscapes and Horizons
11
A comprehensive study on deep learning bug characteristics
12
Generating Adversarial Driving Scenarios in High-Fidelity Simulators
13
Fault Detection Effectiveness of Metamorphic Relations Developed for Testing Supervised Classifiers
14
BugsJS: a Benchmark of JavaScript Bugs
15
Rapidly-exploring Random Trees-based Test Generation for Autonomous Vehicles
16
Towards Structured Evaluation of Deep Neural Network Supervisors
17
DeepGini: Prioritizing Massive Tests to Reduce Labeling Cost
18
Grammar Based Directed Testing of Machine Learning Systems
19
Towards Corner Case Detection for Autonomous Driving
20
Towards Improved Testing For Deep Learning
21
DeepFault: Fault Localization for Deep Neural Networks
22
Paracosm: A Language and Tool for Testing Autonomous Driving Systems
23
DeepCT: Tomographic Combinatorial Testing for Deep Learning Systems
24
Towards Testing of Deep Learning Systems with Training Set Reduction
25
Input Prioritization for Testing Neural Networks
26
A Noise-Sensitivity-Analysis-Based Test Prioritization Technique for Deep Neural Networks
27
A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability
28
Adversarial Sample Detection for Deep Neural Network through Model Mutation Testing
29
Safely Entering the Deep: A Review of Verification and Validation for Machine Learning and a Challenge Elicitation in the Automotive Industry
30
On Testing Machine Learning Programs
31
Dataset Diversity for Metamorphic Testing of Machine Learning Software
32
MODE: automated neural network model debugging via state differential analysis and input selection
33
Secure Deep Learning Engineering: A Software Quality Assurance Perspective
34
Using Ontologies for Test Suites Generation for Automated and Autonomous Driving Functions
35
Adversarial Learning-Based On-Line Anomaly Monitoring for Assured Autonomy
36
TensorFI: A Configurable Fault Injector for TensorFlow Applications
37
Scenic: a language for scenario specification and scene generation
38
Testing Autonomous Cars for Feature Interaction Failures using Many-Objective Search
39
DeepRoad: GAN-Based Metamorphic Testing and Input Validation Framework for Autonomous Driving Systems
40
DLFuzz: differential fuzzing testing of deep learning systems
41
Guiding Deep Learning System Testing Using Surprise Adequacy
42
METTLE: A METamorphic Testing Approach to Assessing and Validating Unsupervised Machine Learning Systems
43
Symbolic Execution for Deep Neural Networks
44
Experimental Resilience Assessment of an Open-Source Driving Agent
45
An empirical study on TensorFlow program bugs
46
Identifying implementation bugs in machine learning based image classifiers using metamorphic testing
47
Testing Untestable Neural Machine Translation: An Industrial Case
48
Automated Directed Fairness Testing
49
TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing
50
MuNN: Mutation Analysis of Neural Networks
51
An AI Software Test Method Based on Scene Deductive Approach
52
SynEva: Evaluating ML Programs by Mirror Program Synthesis
53
Manifesting Bugs in Machine Learning Code: An Explorative Study with Mutation Testing
54
Toward a Holistic Software Systems Engineering Approach for Dependable Autonomous Systems
55
Testing Vision-Based Control Systems Using Learnable Evolutionary Algorithms
56
DeepMutation: Mutation Testing of Deep Learning Systems
57
Quantitative Projection Coverage for Testing ML-enabled Autonomous Systems
58
Concolic Testing for Deep Neural Networks
59
Towards formal methods and software engineering for deep learning: Security, safety and productivity for dl systems development
60
Simulation-based Adversarial Test Generation for Autonomous Vehicles with Machine Learning Components
61
A Survey of Software Quality for Machine Learning Applications
62
DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems
63
Testing Deep Neural Networks
64
Adaptive generation of challenging scenarios for testing and evaluation of autonomous vehicles
65
A comprehensive self-driving car test
66
The role of archives in digital preservation
67
Deep Predictive Models for Collision Risk Assessment in Autonomous Driving
68
CARLA: An Open Urban Driving Simulator
69
Testing of autonomous vehicles using surrogate models and stochastic optimization
70
Observation Based Creation of Minimal Test Suites for Autonomous Vehicles
71
Generalized Oracle for Testing Machine Learning Computer Programs
72
DeepTest: Automated Testing of Deep-Neural-Network-Driven Autonomous Cars
73
Validating a Deep Learning Framework by Metamorphic Testing
74
DeepXplore: Automated Whitebox Testing of Deep Learning Systems
75
Combination and mutation strategies to support test data generation in the context of autonomous vehicles
76
Improve the Quality of ARC Systems Based on the Metamorphic Testing
77
Intelligence Testing for Autonomous Vehicles: A New Approach
78
Testing advanced driver assistance systems using multi-objective search and neural networks
79
On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study
80
The Cityscapes Dataset for Semantic Urban Scene Understanding
81
Deep Residual Learning for Image Recognition
82
Guidelines for conducting systematic mapping studies in software engineering: An update
83
Data Mining with Decision Trees: Theory and Applications
84
Evaluating strategies for study selection in systematic literature studies
85
Very Deep Convolutional Networks for Large-Scale Image Recognition
86
Defects4J: a database of existing faults to enable controlled testing studies for Java programs
87
Guidelines for snowballing in systematic literature studies and a replication in software engineering
88
Deep learning in neural networks: An overview
89
You Are the Only Possible Oracle: Effective Test Selection for End Users of Interactive Machine Learning Systems
90
Data Mining with Decision Trees - Theory and Applications. 2nd Edition
91
An Introduction to Statistical Learning
92
Sound empirical evidence in software testing
93
Testing and validating machine learning classifiers by metamorphic testing
94
Statistical Models: Theory and Practice, Revised Edition by David A. Freedman
95
Automatic system testing of programs without test oracles
96
Introduction to information retrieval
97
Systematic Mapping Studies in Software Engineering
98
Parameterizing random test data according to equivalence classes
99
Cross versus Within-Company Cost Estimation Studies: A Systematic Review
100
k-means++: the advantages of careful seeding
101
Pattern Recognition and Machine Learning
102
Predicting Electricity Distribution Feeder Failures Using Machine Learning Susceptibility Analysis
103
Performing systematic literature reviews in software engineering
104
Requirements engineering paper classification and evaluation criteria: a proposal and a discussion
105
Supporting Controlled Experimentation with Testing Techniques: An Infrastructure and its Potential Impact
107
The elements of statistical learning: data mining, inference and prediction
108
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
109
Automatic Testing of an Autonomous Parking System Using Evolutionary Computation
110
A Unified Framework for Cohesion Measurement in Object-Oriented Systems
111
Support-Vector Networks
112
An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression
113
Mixture models : inference and applications to clustering
114
Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
115
Computing Machinery and Intelligence
116
Artificial Intelligence A Modern Approach 3rd Edition
117
Replication package (2019)
118
Replication package. https:// github.com/testingautomated/deepthoughts
119
ACM, New York, pp 129–140
120
Mining Test Inputs for Autonomous Vehicles
121
VALUATION : A N ADVERSARIAL APPROACH TO UNCOVER CATASTROPHIC FAILURES
122
Deep learning for neural networks
123
UCI machine learning repository (2017)
124
Prescan simulation of adas and active safety
125
Uncertainty in Deep Learning
126
Dataset Coverage for Testing Machine Learning Computer Programs
127
DREBIN: Effective and Explainable Detection of Android Malware in Your Pocket
128
MNIST handwritten digit database
129
Learning Multiple Layers of Features from Tiny Images
130
Systematic literature reviews in software engineering - A systematic literature review
131
Properties of Machine Learning Applications for Use in Metamorphic Testing
132
Improving the Dependability of Machine Learning Applications
133
An Approach to Software Testing of Machine Learning Applications
134
Testing and Analysis : Process , Principles , and Techniques
135
A Unified Framework for Coupling Measurement in Object-Oriented Systems
136
Gradient-based learning applied to document recognition
137
Standard Glossary of Software Engineering Terminology
138
Finding Groups in Data: An Introduction to Cluster Analysis
139
Clustering by means of medoids
140
Pseudo-oracles for non-testable programs
