#ReviewerRecommend ReviewerRecommend is to give the recommended reviewer list, from the file changes of a certain review request.

#Design Principles The purpose of recommendation system is, to recommend items for a certain user.
The general concepts are:

1. Item based recommendation:
   User Mike likes item A, and item A & B are similar, so that to recommend item B to the user Mike.
2. User based recommendation:
   User Mike and user Jim are similar, and Jim likes item C, so that to recommend item C to user Mike.
3. Feature based recommendation:
   User Mike, Jim, Kevin have features Xa, Xb, Xc and liked items A, B, C.
   We can train the model Y=f(X), where Xa, Xb, Xc are X and A, B, C are Y.
   So that, for user Tom with feature Xd, we can use the model f to give the item recommendation.

ReviewerRecommend is the recommendation based on features.
Here,
(1) "User" is the "File changes of a certain review request";
(2) "Item" is the "Recommended reviewer list";
(3) "Features" are X1, X2, ..., X6, where:
X1 -> reciprocal of interval of the last code change;
X2 -> times of changes;
X3 -> average code size for each change;
X4 -> reciprocal of interval of the last code review;
X5 -> times of review;
X6 -> average number of comments raised for each review.

6 typical machine learning algorithms are used to train the model, including Logistic Regression, Decision Tree, Support Vector Machine, Random Forest, AdaBoost, and Naive Bayes.

#Getting started

1. Software needed to install:
   (1) Anaconda: Python machine learning environment
   Anaconda2-4.2.0-Windows-x86_64
   (2) mongoDB: Data base to store code change and review data
   mongodb-win32-x86_64

2. Data source
   (1) The code change base is a local Git repo from GitHub or BitBucket.
   (2) The review base is Pull Requests (PR) records in BitBucket.

3. Settings
   (1) Configurate config.ini with your specialized settings.

#How the project is run

1. Capture review and code change data from Butbucket and Git repo.
   python bitbucket_api.py
   python git_api.py

2. Generate training and test data set with feature vectors, and train model with the typical 6 machine learning algorithms.
   python ML_model.py

3. For the review with id xxx, use the model generated in step 2 to recommend reviewers with top N priority and recommendation reasons.
   python predict_reviewer.py -id xxx

#Output

1. The top N recommended reviewer list:
   E.g. ['Jerry', 'Jim', 'Mike']

2. Recommend reason:
   For each recommended reviewer with file, the reason would be given.
   If the person's code change or review contribution is more than average, a "great" word would be given.

3. Recommend Metrics:
   Precision: true true num / predicted num (N)
   Recall: true true num / really chosen num
   Novelty: Num of "great" that was not chosen by review author originally / total number of reviewers.
   Coverage per 7 days: Within the latest 7 days, num of recommended reviewers / total number of reviewers in the pool.

#Authors Github id ZhangJifei with [email protected]
Github id huangrurong with [email protected]