  Software developers often submit questions to technical Q&A sites like Stack
Overflow (SO) to resolve code-level problems. In practice, they include example
code snippets with questions to explain the programming issues. Existing
research suggests that users attempt to reproduce the reported issues using
given code snippets when answering questions. Unfortunately, such code snippets
could not always reproduce the issues due to several unmet challenges that
prevent questions from receiving appropriate and prompt solutions. One previous
study investigated reproducibility challenges and produced a catalog. However,
how the practitioners perceive this challenge catalog is unknown.
Practitioners' perspectives are inevitable in validating these challenges and
estimating their severity. This study first surveyed 53 practitioners to
understand their perspectives on reproducibility challenges. We attempt to (a)
see whether they agree with these challenges, (b) determine the impact of each
challenge on answering questions, and (c) identify the need for tools to
promote reproducibility. Survey results show that - (a) about 90% of the
participants agree with the challenges, (b) "missing an important part of code"
most severely hurt reproducibility, and (c) participants strongly recommend
introducing automated tool support to promote reproducibility. Second, we
extract \emph{nine} code-based features (e.g., LOC, compilability) and build
five Machine Learning (ML) models to predict issue reproducibility. Early
detection might help users improve code snippets and their reproducibility. Our
models achieve 84.5% precision, 83.0% recall, 82.8% F1-score, and 82.8% overall
accuracy, which are highly promising. Third, we systematically interpret the ML
model and explain how code snippets with reproducible issues differ from those
with irreproducible issues.
