Intelligent Career Path Recommendation Model in Software Engineering Using Machine Learning

Abstract

The rapid technological advancements in software engineering have made choosing an appropriate job role increasingly challenging, calling for the need of intelligent and data-driven guidance solutions. This study presents a machine learning model designed to assist software engineers in identifying the most suitable technology career paths based on their interests, skill sets, and inclinations. The proposed model utilizes the Logistic Boosting Regression (LogitBoost) method to recommend optimal job roles based on responses to a set of questions related to various technology areas, including mobile app development, cybersecurity, data analysis, and front-end/back-end development. The study also provides valuable perspectives into the key factors that affect career choices within the software engineering domain. Experimental results obtained from a survey dataset demonstrated the effectiveness of the proposed approach, achieving a classification accuracy of 87.89%.

Keywords

• career path recommendation
• classification
• machine learning
• software engineering

Yazılım Mühendisliğinde Makine Öğrenmesi Kullanarak Akıllı Kariyer Yolu Öneri Modeli

Abstract

Yazılım mühendisliğindeki hızlı teknolojik gelişmeler, uygun bir iş rolü seçmeyi giderek daha zorlu hale getirmiş ve akıllı ve veri odaklı rehberlik çözümlerine olan ihtiyacı ortaya çıkarmıştır. Bu çalışma, yazılım mühendislerinin ilgi alanlarına, beceri setlerine ve eğilimlerine göre en uygun teknoloji kariyer yollarını belirlemelerine yardımcı olmak üzere tasarlanmış bir makine öğrenmesi modelini sunmaktadır. Önerilen model, mobil uygulama geliştirme, siber güvenlik, veri analizi ve ön uç/arka uç geliştirme dahil olmak üzere çeşitli teknoloji alanlarıyla ilgili bir dizi soruya verilen yanıtlara dayanarak en uygun iş rollerini önermek için Lojistik Artırma Regresyonu (LogitBoost) yöntemini kullanmaktadır. Çalışma ayrıca, yazılım mühendisliği alanındaki kariyer seçimlerini etkileyen temel faktörlere ilişkin değerli bakış açıları sunmaktadır. Bir anket veri setinden elde edilen deneysel sonuçlar, önerilen yaklaşımın etkinliğini göstermiş ve %87,89'luk bir sınıflandırma doğruluğuna ulaşmıştır.

Keywords

• kariyer yolu önerme
• sınıflandırma
• makine öğrenmesi
• yazılım mühendisliği

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