Time complexity measures how an algorithm's runtime increases as the input size grows, which is a key factor in evaluating efficiency. Other factors, like readability, are important for maintainability but not efficiency.
The time a developer takes to solve a problem reflects their understanding and ability to implement solutions efficiently. However, other metrics, like correctness and optimality, are also considered.
A correctness score of 100% means the code solved all the given test cases, including edge cases, as expected. This doesn't necessarily mean the code is the fastest or most concise.
Skill percentile indicates how a developer's performance compares to others. For example, being in the 80th percentile means performing better than 80% of participants.
Submission history allows developers and reviewers to track how skills evolve, identify strengths, and pinpoint areas for improvement, fostering consistent growth.
