  ChatGPT has revolutionized many research and industrial fields. ChatGPT has
shown great potential in software engineering to boost various traditional
tasks such as program repair, code understanding, and code generation. However,
whether automatic program repair (APR) applies to deep learning (DL) programs
is still unknown. DL programs, whose decision logic is not explicitly encoded
in the source code, have posed unique challenges to APR. While to repair DL
programs, an APR approach needs to not only parse the source code syntactically
but also needs to understand the code intention. With the best prior work, the
performance of fault localization is still far less than satisfactory (only
about 30\%). Therefore, in this paper, we explore ChatGPT's capability for DL
program repair by asking three research questions. (1) Can ChatGPT debug DL
programs effectively? (2) How can ChatGPT's repair performance be improved by
prompting? (3) In which way can dialogue help facilitate the repair? On top of
that, we categorize the common aspects useful for prompt design for DL program
repair. Also, we propose various prompt templates to facilitate the performance
and summarize the advantages and disadvantages of ChatGPT's abilities such as
detecting bad code smell, code refactoring, and detecting API
misuse/deprecation.
