  Question answering plays a pivotal role in human daily life because it
involves our acquisition of knowledge about the world. However, due to the
dynamic and ever-changing nature of real-world facts, the answer can be
completely different when the time constraint in the question changes.
Recently, Large Language Models (LLMs) have shown remarkable intelligence in
question answering, while our experiments reveal that the aforementioned
problems still pose a significant challenge to existing LLMs. This can be
attributed to the LLMs' inability to perform rigorous reasoning based on
surface-level text semantics. To overcome this limitation, rather than
requiring LLMs to directly answer the question, we propose a novel approach
where we reframe the $\textbf{Q}$uestion $\textbf{A}$nswering task
$\textbf{a}$s $\textbf{P}$rogramming ($\textbf{QAaP}$). Concretely, by
leveraging modern LLMs' superior capability in understanding both natural
language and programming language, we endeavor to harness LLMs to represent
diversely expressed text as well-structured code and select the best matching
answer from multiple candidates through programming. We evaluate our QAaP
framework on several time-sensitive question answering datasets and achieve
decent improvement, up to $14.5$% over strong baselines. Our codes and data are
available at https://github.com/TianHongZXY/qaap
