  Non-functional bugs (e.g., performance- or accuracy-related bugs) in Deep
Learning (DL) frameworks can lead to some of the most devastating consequences.
Reporting those bugs on a repository such as GitHub is a standard route to fix
them. Yet, given the growing number of new GitHub reports for DL frameworks, it
is intrinsically difficult for developers to distinguish those that reveal
non-functional bugs among the others, and assign them to the right contributor
for investigation in a timely manner. In this paper, we propose MHNurf - an
end-to-end tool for automatically identifying non-functional bug related
reports in DL frameworks. The core of MHNurf is a Multifaceted Hierarchical
Attention Network (MHAN) that tackles three unaddressed challenges: (1)
learning the semantic knowledge, but doing so by (2) considering the hierarchy
(e.g., words/tokens in sentences/statements) and focusing on the important
parts (i.e., words, tokens, sentences, and statements) of a GitHub report,
while (3) independently extracting information from different types of
features, i.e., content, comment, code, command, and label.
  To evaluate MHNurf, we leverage 3,721 GitHub reports from five DL frameworks
for conducting experiments. The results show that MHNurf works the best with a
combination of content, comment, and code, which considerably outperforms the
classic HAN where only the content is used. MHNurf also produces significantly
more accurate results than nine other state-of-the-art classifiers with strong
statistical significance, i.e., up to 71% AUC improvement and has the best
Scott-Knott rank on four frameworks while 2nd on the remaining one. To
facilitate reproduction and promote future research, we have made our dataset,
code, and detailed supplementary results publicly available at:
https://github.com/ideas-labo/APSEC2022-MHNurf.
