Evaluating Developers’ Expertise in Serverless Functions by Mining Activities from Multiple Platforms

Abstract-- In the domain of software development, the evaluation of developer expertise has gained prominence, particularly with the rise of serverless functions. These functions, which simplify the development process by delegating infrastructure management to cloud providers, are becoming more common. As developers may utilize functions created by their peers, understanding the expertise of the original developer is crucial since it can serve as an indicator of the functions' quality. While there are existing methods for expertise evaluation, certain gaps remain, especially concerning serverless functions. To address this, our research aims to enhance the assessment of developer expertise in this area by extracting activity-based features from both GitHub and Stack Overflow. After processing the extracted data, we applied various machine learning algorithms. Our findings suggest a potential improvement in evaluating developer expertise when incorporating features from Stack Overflow compared to using only GitHub data. The extent of this improvement was observed to differ among programming languages, with variations in accuracy improvement percentages ranging from ۲% to ۱۹%. This study contributes to the ongoing discourse on developer expertise evaluation, highlighting the potential benefits of drawing from multiple data sources.

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Keywords-- Developer Expertise Evaluation ، Data analysis ، Machine Learning Algorithms ، Serverless Functions ، Software Development