Building Implicit Vector Representations of Individual Coding Style
This addresses the problem of tracking knowledge transfer in software development teams, but it is incremental as it builds on existing authorship recognition techniques.
The paper tackled the problem of representing individual coding styles to facilitate team collaboration by proposing an implicit vector representation method, finding that developers who report learning from each other are represented closer to each other.
With the goal of facilitating team collaboration, we propose a new approach to building vector representations of individual developers by capturing their individual contribution style, or coding style. Such representations can find use in the next generation of software development team collaboration tools, for example by enabling the tools to track knowledge transfer in teams. The key idea of our approach is to avoid using explicitly defined metrics of coding style and instead build the representations through training a model for authorship recognition and extracting the representations of individual developers from the trained model. By empirically evaluating the output of our approach, we find that implicitly built individual representations reflect some properties of team structure: developers who report learning from each other are represented closer to each other.