  Significant interest in applying Deep Neural Network (DNN) has fueled the
need to support engineering of software that uses DNNs. Repairing software that
uses DNNs is one such unmistakable SE need where automated tools could be
beneficial; however, we do not fully understand challenges to repairing and
patterns that are utilized when manually repairing DNNs. What challenges should
automated repair tools address? What are the repair patterns whose automation
could help developers? Which repair patterns should be assigned a higher
priority for building automated bug repair tools? This work presents a
comprehensive study of bug fix patterns to address these questions. We have
studied 415 repairs from Stack overflow and 555 repairs from Github for five
popular deep learning libraries Caffe, Keras, Tensorflow, Theano, and Torch to
understand challenges in repairs and bug repair patterns. Our key findings
reveal that DNN bug fix patterns are distinctive compared to traditional bug
fix patterns; the most common bug fix patterns are fixing data dimension and
neural network connectivity; DNN bug fixes have the potential to introduce
adversarial vulnerabilities; DNN bug fixes frequently introduce new bugs; and
DNN bug localization, reuse of trained model, and coping with frequent releases
are major challenges faced by developers when fixing bugs. We also contribute a
benchmark of 667 DNN (bug, repair) instances.
