With a comparatively small training dataset, the models are able to achieve F-1 scores of 77% and 85.31%, respectively. In this paper, we demonstrate how the models can choose the best quality attributes and assign the code review to the most qualified reviewers. Finally we selected the recommended reviewer based on the distance between a commit and candidate reviewers. We then positioned the reviewers based on their historical data and the quality attributes characteristics. Two models are built and trained for automating the classification of the commits based on their quality attributes using the manual labeling of commits and multi-class classifiers. We first designed machine learning schemes for abstracting quality attributes based on historical data from the OpenStack repository. To achieve this, quality attributes are classified and abstracted from the commit messages and based on which, the commits are assigned to the reviewers with the capability in reviewing the target commits. In this study, we target on auto reviewer assignment in large scale software stacks and aim to build a self-learning, and self-correct platform for intelligently matching between a commit based on its quality attributes and the skills sets of reviewers. Quality attributes serve as the connection among the user requirements, delivered function description, software architecture and implementation through put the entire software stack cycle. The reviewers who have the specialty in both functioning (domain knowledge) and non-functioning areas of a commit are considered as the most qualified reviewer to look over any changes to the code. Additionally, a review by someone who lacks knowledge and understanding of the code can result in high resource consumption and technical errors. Finding the best reviewer for a code change, however, is extremely challenging especially in large scale, especially open source software stacks with cross functioning designs and collaborations among multiple developers and teams. By identifying problems before they arise in production, it enhances the quality of the code. That being said, the application is not very configurable and although it does have an excellent selection of features and a good help section, the lack of these options lets Nitro Pro down somewhat.In the process of developing software, code review is crucial. It comes with an in-built conversion tool, advanced reviewing tools, the ability to create new PDF documents, advanced security features and even OCR. Overall, Nitro Pro contains all the tools that you will require for working with PDF documents.
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