Abstract:
Distributed Software development process has dramatically changed over the last decade due to the integration of social collaborative development environment. The pull-based software development methodology made its mark in the open source distributed development as it is a convenient and effective system to organise collaborative contribution. Code reviews for software projects have been a best practice in software engineering. With the emerge of pull-based software development methodology, code reviewers faced difficulty in reviewing the contributions because of the higher number of incoming pull requests. In order to address this problem, reviewer recommendation systems have been implemented. In these systems, textual data mining techniques have been applied. This paper focuses on identifying the different approaches in terms of textual data mining used in the domain of the reviewer recommendations in pull-based software development and identifies their drawbacks and room for improvement. This paper contains the initial part of ongoing research and in the future, we hope to use this knowledge to come up with a solution that addresses the identified drawbacks and the identified improvements.
