Scholarlink

In large-scale software development ecosystems, the intricate web of developer interactions serves as a framework for cooperative endeavors. This study explores the multifaceted landscape of developer relationships, aiming to uncover the pivotal factors that drive these interactions. We were able to model the dynamic network structures and the evolving patterns of tie formation over time using Temporal Exponential Random Graph Models with Bootstrapped Pseudolikelihood (BTERGMs) in two different software ecosystems. Through meticulous analysis using advanced statistical models, we identified the developer attributes that have the greatest impact on the creation and evolution of these networks. A key novel insight from our analysis is the identification of PageRank as a significant factor shaping developer interactions over time. This discovery has profound implications for individual developers, project managers, and organizational decision-makers, offering valuable insights for navigating the complexities of contemporary software development ecosystems.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

What Drives the Variation of Developer Communication Characteristics Over Time? An Empirical Study Across Multiple Datasets

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Suchandra Chakraborty,
  • Ankan Basu,
  • Aritra Saha,
  • Ishita Bardhan,
  • Subhajit Datta,
  • Subhashis Majumder

摘要

In large-scale software development ecosystems, the intricate web of developer interactions serves as a framework for cooperative endeavors. This study explores the multifaceted landscape of developer relationships, aiming to uncover the pivotal factors that drive these interactions. We were able to model the dynamic network structures and the evolving patterns of tie formation over time using Temporal Exponential Random Graph Models with Bootstrapped Pseudolikelihood (BTERGMs) in two different software ecosystems. Through meticulous analysis using advanced statistical models, we identified the developer attributes that have the greatest impact on the creation and evolution of these networks. A key novel insight from our analysis is the identification of PageRank as a significant factor shaping developer interactions over time. This discovery has profound implications for individual developers, project managers, and organizational decision-makers, offering valuable insights for navigating the complexities of contemporary software development ecosystems.