COMP 5704: Parallel Algorithms and Applications in Data Science

School of Computer Science
Carleton University, Ottawa, Canada

Project Title: Parallel Genetic Algorithm for GitHub Projects Recommendation

Name: Lance Wang (#101144671)


Project Outline:

In the era of big data, parallel computing has been widely used to overcome the compu- tational barriers. Techniques of multicore processors, cloud computing and peer-to-peer (P2P) are becoming mature, which enable many researchers and developers to obtain vast computational resources, which made evolutionary computing (EC, Fogel:107769) became the popular area that is widely used to solve and optimise solutions for different problems. Genetic algorithm (GA) has been a popular solution finding and optimising approach in computer science. Parallel Genetic Algorithm (PGA) is also one of the trending research area since GA can be parallelized and distributed straightforwardly.

Since GitHub was launched on 2008, tremendous developers have devoted themselves to this open-source software (OSS) platform for creating countless state-of-the-art software and techniques. Gradually, developers not only build professional skills (i.e. expertise) but also communities (i.e. social networks) inside GitHub. However, as the number of projects (or called repositories) and users are increasing dramatically, finding new projects to contribute is no longer as easy as it was. Therefore, in this research, we aim to build a GitHub repositories recommender system by using PGA to find next appropriate projects for developers based on their expertise and social networks.

Startup Paper(s):


Relevant References: