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About this paper
Md Imranur Rahman Akib, Fathima Binthe Muhammed, Umit Saha, Md Fazlul Karim Patwary, Mehrin Anannya, Md Alomgeer Hussein, Md Biplob Hosen
This study developed a real-time, data-driven system to predict the software engineering job readiness of competitive programmers using their Codeforces activity. The system, leveraging a Random Forest model, achieved 88.8% accuracy in classifying users into four distinct employability levels.
Key insights
- Competitive programming performance, particularly user ratings and consistent contest participation, strongly correlates with career prospects in software engineering.
- The Random Forest classifier demonstrated superior performance in multi-class job readiness prediction compared to SVM and KNN.
- Problem categories like 'Greedy,' 'Dynamic Programming,' and 'Implementation' were identified as fundamentally important based on their frequent solution by users.
Summary by alphaXiv.
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