Hardcoded Outputs Undermine Coding Assessments
The article discusses the issue of gaming coding assessments by hardcoding expected outputs instead of solving the problem algorithmically. This undermines the integrity of the assessment and the ability to measure true skill.
Why it matters
Coding assessments are crucial for evaluating technical skills, and if they can be gamed, they lose their value as a reliable measure of a candidate's abilities.
Key Points
- 1Hardcoding outputs for known hidden tests instead of solving the problem algorithmically
- 2This is an assessment integrity issue, not just a leaderboard problem
- 3Genuine solutions involve training on data, building features, fitting a model, and making predictions
Details
The article focuses on a machine learning challenge on HackerRank where the author noticed that some top-scoring submissions appeared to hardcode outputs for known hidden tests instead of solving the problem algorithmically. This is problematic because if a platform can be gamed by memorizing test cases, the score stops measuring the true skill of the candidate. The article provides a visual comparison of a genuine solution path, which involves training on the provided data, building features, fitting a model, and making predictions, versus the anti-pattern of hardcoding expected outputs based on test size. The author argues that this undermines the integrity of the assessment and the ability to accurately evaluate the candidates' skills.
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