Required Conditions

• Correctness
• Robustness
• Fairness
• Privacy
• Efficiency

ML Items

• Framework
• Data
• Learning Algorithm

Testing Activities

• Test Input Generation
• Test Oracle Identification
• Test Adequacy Evaluation
• Bug Triage

Testing Properties

• Security
  • Model Extraction
• Data Privacy 
  • Linkage Attack
• Efficiency
  • Slow / Infinite Loop
  • VGG-19 in mobile device
• Interpretability
  • Transparency
  • Post-hoc explanations

Robustness

• Correctness in the presence of noise
• DeepFool
  • Point-wise robustness
  • Adversarial Frequency
  • Adversarial Severity
    • distance between input and adversarial example
• DeepSafe
  • A cluster should have same label

Overfitting

• Cross-validation
  • fails if test data is unrepresentative of potential unseen data
• Perturbed Model Validation (PMV)
  • Both underfitting and overfitting are less sensitive to noise
• Generate adversarial examples from test data
  • If error increases on adversarial examples
    • Overfitting

Efficiency

• Training data reduction approaches
  • Smaller subset of data
  • Faster ML testing

Test Input Generation

• Inputs for text classification
  • Grammar
  • Distance between inputs
• NLI 
  • Mutate sentences for robustness testing
• DeepCheck
  • DNN -> Program
• LIME, DeepConcolic

Metamorphic Test Oracles

• Perturbed Model Validation (PMV)
  • ​inject noise into training data
  • Overfitting degree is less sensitive to noise
• Image Transformation to weather conditions
  • Steering angle shouldn't change significantly
• Classification consistency among similar images

Metamorphic Test Oracles

• Metamorphic relations between datasets
  • ​P(training_data) == P(new_data)
  • Automate Metamorphic relations to detect bugs
    • Amsterdam
    • Corduroy​

Cross-referencing as Oracle

• Differential Testing
  • ​2 similar applications should respond to an input with similar outputs
• ​Mirror program
  • ​​Program that represents the training data
  • Similar behaviour on test data

Metrics for Oracle Design

• Flickering Test
  • along the boundary of the road
• Nested car boundary

Test Adequacy

• Layer-level coverage
  • Top hyperactive neurons and their combinations
• Surprise Adequacy
  • Kernel Density Estimation
    • approximate likelihood of system having seen similar input during training
  • Neuron Activation trace (vector)
    • Distance between input and training data

Mutation Testing

• Inject faults
• Minor perturbations on decision boundaries of DNN
• Mutation score
  • (ratio) #instances where results changed

Rule-based Adequacy Test

• Training should be reproducible
• All features should be useful
• A simpler equally successful model shouldn't exist

ML Testing Components

• Bugs in Data
  • Skew Detection
    • KDE
    • Neural Activation trace

Data Bug Detection Framework

• Data Validation System
  • Integral part of Google's TFX
  • Metric : distance between training data and new data
• ActiveClean : Iterative data cleaning
• BoostClean : Domain value violation
• ...

Data Bug Detection Framework

• Automatic 'Unit' Testing
  • Common, user-defined quality constraints for data testing
• AlphaClean
  • Greedy tree search to automatically tune the parameters of data cleaning pipelines

Bug Detection in Frameworks

• Incorrect implementation of algorithms
• Differential testing across multiple implementations
• Metamorphic testing
  • normalize/scale input data
• MutPy
  • Inject mutants to simulate implementation bugs