Turtle Score -- Similarity Based Developer Analyzer

TL;DR

This paper introduces Turtle Score, a machine learning-based developer similarity analyzer designed to assist IT recruiters in identifying candidates with compatible working patterns and personalities to enhance team productivity.

Contribution

It presents a novel similarity-based model that analyzes developer data to improve recruitment accuracy by matching candidates with similar performance and personality traits.

Findings

High accuracy in candidate matching

Improved recruitment efficiency

Enhanced team productivity

Abstract

In day-to-day life, a highly demanding task for IT companies is to find the right candidates who fit the companies' culture. This research aims to comprehend, analyze and automatically produce convincing outcomes to find a candidate who perfectly fits right in the company. Data is examined and collected for each employee who works in the IT domain focusing on their performance measure. This is done based on various different categories which bring versatility and a wide view of focus. To this data, learner analysis is done using machine learning algorithms to obtain learner similarity and developer similarity in order to recruit people with identical working patterns. It's been proven that the efficiency and capability of a particular worker go higher when working with a person of a similar personality. Therefore this will serve as a useful tool for recruiters who aim to recruit people with high productivity. This is to say that the model designed will render the best outcome possible with high accuracy and an immaculate recommendation score.