Abstract
During the software development life cycle, the turnover of software developers is one of the critical risks that may lead to severe problems (such as postponement and failure of projects), which is often ignored by many professionals. To address this problem, we focus on the uncertainty of turnover risk of software developer (TRSD) and its loss incurred to projects. To tackle this problem, we propose a method to quantify the uncertain risks related to developer turnover, including resignation and replacement. Additionally, to calculate the extent of loss caused by TRSD, we employed machine learning, natural language processing, and data mining techniques to identify software development activities and establish the importance of developers by mining and analyzing the commit event logs. Moreover, based on the information entropy theory, we established a risk measurement model of TRSD that can be used to measure the risk level of each developer and the holistic risk of ongoing software projects. Finally, we validated the feasibility and efficacy through a case study.
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