Understanding and predicting method-level source code changes using commit history data

Software development and software maintenance require a large amount of source code changes to be made to a software repositories. Any change to a repository can introduce new resource needs which will cost more time and money to the repository owners. Therefore it is useful to predict future code changes in an effort to help determine and allocate resources. We are proposing a technique that will predict whether elements within a repository will change in the near future given the development history of the repository. The development history is collected from source code management tools such as GitHub and stored local in a PostgreSQL. The predictions are developed using the machine learning approaches Support Vector Machine and Random Forest. Furthermore, we will investigate what factors have the most impact on the performance of predicting using either Support Vector Machines or Random Forest with future code changes using commit history. Visualizations were used as part of the approach to gain a deeper understanding of each repository prior to making predictions. To validate the results we analyzed open source Java software repositories including; acra, storm, fresco, dagger, and deeplearning4j.

Name thesis:degree_name

Master of Science (MSc)

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Ontario Institute of Technology

Year dc:date.issued

2016

Author dc:creator

• Heron, Joseph

Advisor dc:contributor.advisor

• Bradbury, Jeremy

• Data mining
• Software analytics
• Machine learning
• Resource management
• Software development

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10155/730

OAI identifier oai:identifier

oai:ontariotechu.scholaris.ca:10155/730