  Predicting the runtime complexity of a programming code is an arduous task.
In fact, even for humans, it requires a subtle analysis and comprehensive
knowledge of algorithms to predict time complexity with high fidelity, given
any code. As per Turing's Halting problem proof, estimating code complexity is
mathematically impossible. Nevertheless, an approximate solution to such a task
can help developers to get real-time feedback for the efficiency of their code.
In this work, we model this problem as a machine learning task and check its
feasibility with thorough analysis. Due to the lack of any open source dataset
for this task, we propose our own annotated dataset CoRCoD: Code Runtime
Complexity Dataset, extracted from online judges. We establish baselines using
two different approaches: feature engineering and code embeddings, to achieve
state of the art results and compare their performances. Such solutions can be
widely useful in potential applications like automatically grading coding
assignments, IDE-integrated tools for static code analysis, and others.
