A Large-Scale Study on Source Code Reviewer Recommendation

This project deals with optimal recommendations of source code reviewers for open source projects. It contains the implementation of RevFinder algorithm (http://ieeexplore.ieee.org/document/7081824/), ReviewBot algorithm (https://labs.vmware.com/download/198/) and of a novel Naive Bayes-based Code Reviewers Recommendation Algorithm.

If you use the provided source code and data set, please cite:

Lipcak, J., Rossi, B. (2018) A Large-Scale Study on Source Code Reviewer Recommendation, in 44th Euromicro Conference on Software Engineering and Advanced Applications (SEAA) 2018, IEEE.

Build and Run:

The aplication has the following requirements:

• Apache Maven has to be installed.
• JDK (at least version 8) has to be installed and JAVA_HOME environment variable has to be set and point to the JDK installation.
• MySQL server running locally (port number and name of schema is specified in aplication.properties)

Deployment:

After the aforementioned steps are done, the application can be compiled and launched from its root folder using the following command:

• mvn spring-boot:run

Usage:

Application can be accessed via /recommend-reviewer endpoint e.g.: http://localhost:6060/api/recommend-reviewer?project-name=metals&project-url=https://api.github.com/repos/scalameta/metals&pr-id=4669&method=BAYES