  Deep Learning models have become an integrated component of modern software
systems. In response to the challenge of model design, researchers proposed
Automated Machine Learning (AutoML) systems, which automatically search for
model architecture and hyperparameters for a given task. Like other software
systems, existing AutoML systems suffer from bugs. We identify two common and
severe bugs in AutoML, performance bug (i.e., searching for the desired model
takes an unreasonably long time) and ineffective search bug (i.e., AutoML
systems are not able to find an accurate enough model). After analyzing the
workflow of AutoML, we observe that existing AutoML systems overlook potential
opportunities in search space, search method, and search feedback, which
results in performance and ineffective search bugs. Based on our analysis, we
design and implement DREAM, an automatic debugging and repairing system for
AutoML systems. It monitors the process of AutoML to collect detailed feedback
and automatically repairs bugs by expanding search space and leveraging a
feedback-driven search strategy. Our evaluation results show that DREAM can
effectively and efficiently repair AutoML bugs.
