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Ontario Tech acknowledges the lands and people of the Mississaugas of Scugog Island First Nation.

We are thankful to be welcome on these lands in friendship. The lands we are situated on are covered by the Williams Treaties and are the traditional territory of the Mississaugas, a branch of the greater Anishinaabeg Nation, including Algonquin, Ojibway, Odawa and Pottawatomi. These lands remain home to many Indigenous nations and peoples.

We acknowledge this land out of respect for the Indigenous nations who have cared for Turtle Island, also called North America, from before the arrival of settler peoples until this day. Most importantly, we acknowledge that the history of these lands has been tainted by poor treatment and a lack of friendship with the First Nations who call them home.

This history is something we are all affected by because we are all treaty people in Canada. We all have a shared history to reflect on, and each of us is affected by this history in different ways. Our past defines our present, but if we move forward as friends and allies, then it does not have to define our future.

Learn more about Indigenous Education and Cultural Services

Computer Science Seminar (Oct. 12, 2016)

Title: Understanding and Predicting Method-level Source Code Changes Using Commit History Data

Speaker: Joseph Heron, MSc Student, Computer Science, University of Ontario Institute of Technology

Abstract: 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.

Biography: Joseph Heron received his undergraduate degree in Software Engineering from the University of Ontario Institute of Technology in 2014. He is currently an MSc Computer Science student in the Software Quality Research Lab under the supervision of Dr. Jeremy Bradbury.