Integrating Pull Request Comment Analysis and Developer Profiles for Expertise-Based Recommendations in Global Software Development

Determining a suitable software developer to match project needs within the Global software development (GSD) context requires detailed information. The complexity of this problem arises from the required combination of the developer's level of technical expertise, domain knowledge, and the extent to which they possess the collaborative skills necessary for a successful project. Typical developer recommendation systems do not consider the dynamics of expertise and cooperative nature of the tasks for assessing their correctness, often restricting themselves to extracting review comments only to measure their usefulness and suggest reviewers. This research intends to create a recommendation system using pull request review comments and selected data from developers' profiles to recommend better experts based on their dynamic expertise. Using advanced algorithm techniques, the proposed model Global Developer Expertise Recommendation System (GDERS) aims to improve the quality of captured data and substantially increase the accuracy of developer recommendations. Impressively, the proposed model significantly outperformed all other text-based classifiers TextCNN, TextRCNN, and Bilstm in this study, showing an accuracy of 91.85%. This research provides a significant achievement of recommendation systems in the global software development context that support more effective collaboration and increase the probability of project completion on time by allowing project managers to find easily accessible developers in the field with the right expertise.