Identification and Analysis of Common Issues Across Different Python Repositories

Abstract

Our main goal for this research is to identify the common issues present in different repositories having python as their primary programming language. A lot of research has been done in the domain of issues present in GitHub repositories. The motivation behind this work is that if similar types of issues are present in different repositories, then solving that issue can be fruitful for more than one repository. Another benefit is that if any new repository is being constructed, owners can have an idea about existing issues in that field and can progress, keeping that in mind. Research has come up with different results such as issues that do have emotions in them, issues that affect the software development process, developers’ working efficiency, etc. In this thesis work, we identify and analyze the common issues existing across different python repositories. We have designed a dataset consists of issues fetched from the REST API provided by GitHub. The analysis is done by applying the NLP techniques for the text specified in the Git comments. Additionally, we have also categorized the common issues based on comments posted by the contributors and the set of issues present across different repositories.