A Developer-Centric Machine Learning Framework for Continuous Burnout Risk Analysis

In the contemporary world that is highly technological, software development has emerged as one of the most challenging careers. The developers are sometimes subjected to frenetic pressure because of the deadlines, complicated problem-solving problems, the code marathon, and learning on the job. Such aspects often lead to developer burnout, which is a mental state of emotional exhaustion, loss of productivity, motivation, and job satisfaction. Burnout does not only have an impact on the mental health of developers but also has a detrimental effect on the productivity and stability of software development organizations as a whole. The main methods used in traditional methods of diagnosing burnout include periodic survey, manual and selfreport surveys. The conventional methods of burnout identification mainly depend on a survey periodically, hand assessment or self-administered questionnaire. Though these techniques offer certain information on the well-being of the employees, they are constrained by the fact that they cannot adapt to the real-time behaviour trends and cannot consistently track the activities of the developers. Moreover, the responses of the surveys might be biased or even incomplete at times, making the burnout analysis less reliable. Moreover, the system will have a real-time monitoring dashboard that will display the burnout risk scores, critical contributing factors, and real-time warning signs to organizations. With its ability to offer ongoing supervision and the explicable insights, the proposed framework will help organizations to actively combat developer burnout, enhance productivity, and establish a healthier workplace.