Machine Learning Models for the Early Detection of Burnout in Software Engineering: a Systematic Literature Review

2026-03-24

AI Summary

This study addresses the lack of systematic evaluation regarding the effectiveness of machine learning approaches for early detection of burnout among software engineers. Through a comprehensive systematic literature review, it provides the first thorough synthesis and comparative analysis of emotion-dimension-based machine learning models and the affective datasets employed to evaluate them. The findings reveal that most existing methods rely heavily on emotion-related features for prediction and identify specific models and datasets that demonstrate superior performance and greater expressiveness. By establishing a reproducible benchmark and offering empirical evidence, this work not only clarifies the current state of the field but also outlines actionable directions for future research in affect-aware burnout prediction within software engineering contexts.

Technology Category

• Cognitive Modeling & Cognitive Systems: Affective Computing
• Machine Learning: Hardware-aware ML
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Burnout is an occupational syndrome that, like many other professions, affects the majority of software engineers. Past research studies showed important trends, including an increasing use of machine learning techniques to allow for an early detection of burnout. This paper is a systematic literature review (SLR) of the research papers that proposed machine learning (ML) approaches, and focused on detecting burnout in software developers and IT professionals. Our objective is to review the accuracy and precision of the proposed ML techniques, and to formulate recommendations for future researchers interested to replicate or extend those studies. From our SLR we observed that a majority of primary studies focuses on detecting emotions or utilise emotional dimensions to detect or predict the presence of burnout. We also performed a cross-sectional study to detect which ML approach shows a better performance at detecting emotions; and which dataset has more potential and expressivity to capture emotions. We believe that, by identifying which ML tools and datasets show a better performance at detecting emotions, and indirectly at identifying burnout, our paper can be a valuable asset to progress in this important research direction.

Problem

• burnout
• machine learning
• software engineering
• early detection
• emotion detection

Innovation

• machine learning
• burnout detection
• systematic literature review
• emotion recognition
• software engineers