  Context: Machine learning (ML) may enable effective automated test
generation.
  Objective: We characterize emerging research, examining testing practices,
researcher goals, ML techniques applied, evaluation, and challenges.
  Methods: We perform a systematic mapping on a sample of 124 publications.
  Results: ML generates input for system, GUI, unit, performance, and
combinatorial testing or improves the performance of existing generation
methods. ML is also used to generate test verdicts, property-based, and
expected output oracles. Supervised learning - often based on neural networks -
and reinforcement learning - often based on Q-learning - are common, and some
publications also employ unsupervised or semi-supervised learning.
(Semi-/Un-)Supervised approaches are evaluated using both traditional testing
metrics and ML-related metrics (e.g., accuracy), while reinforcement learning
is often evaluated using testing metrics tied to the reward function.
  Conclusion: Work-to-date shows great promise, but there are open challenges
regarding training data, retraining, scalability, evaluation complexity, ML
algorithms employed - and how they are applied - benchmarks, and replicability.
Our findings can serve as a roadmap and inspiration for researchers in this
field.
