  Deep learning (DL) techniques have gained significant popularity among
software engineering (SE) researchers in recent years. This is because they can
often solve many SE challenges without enormous manual feature engineering
effort and complex domain knowledge. Although many DL studies have reported
substantial advantages over other state-of-the-art models on effectiveness,
they often ignore two factors: (1) replicability - whether the reported
experimental result can be approximately reproduced in high probability with
the same DL model and the same data; and (2) reproducibility - whether one
reported experimental findings can be reproduced by new experiments with the
same experimental protocol and DL model, but different sampled real-world data.
Unlike traditional machine learning (ML) models, DL studies commonly overlook
these two factors and declare them as minor threats or leave them for future
work. This is mainly due to high model complexity with many manually set
parameters and the time-consuming optimization process. In this study, we
conducted a literature review on 93 DL studies recently published in twenty SE
journals or conferences. Our statistics show the urgency of investigating these
two factors in SE. Moreover, we re-ran four representative DL models in SE.
Experimental results show the importance of replicability and reproducibility,
where the reported performance of a DL model could not be replicated for an
unstable optimization process. Reproducibility could be substantially
compromised if the model training is not convergent, or if performance is
sensitive to the size of vocabulary and testing data. It is therefore urgent
for the SE community to provide a long-lasting link to a replication package,
enhance DL-based solution stability and convergence, and avoid performance
sensitivity on different sampled data.
