Machine Learning for Software Engineering

By John Pangas

Elevator Pitch

Join me as I delve into the world of Python and machine learning, showcasing Mozilla’s automation magic on tracking bugs! I’ll also unveil new research on using Python and LLMs to create automated test plans, ensuring seamless Firefox releases. Plus, hear my inspiring Outreachy journey.

Description

Discover how Python and machine learning are transforming software engineering in my talk highlighting Mozilla’s open-source project, Bugbug. I’ll take you on a journey through innovative solutions for tagging and tracking bugs, test selection, and defect prediction on Mozilla Products including the famous browser, Mozilla Firefox.

Additionally, you will see through new applied research how leveraging Python and large language models (LLMs) is making it possible to create automated test plans that help QA teams discover potential issues before a release of a new feature in Mozilla Firefox.

This talk will not only delve into the technical intricacies of these advanced methodologies but also highlight how Python’s simplicity and versatility make it the perfect tool for such cutting-edge innovations. I will showcase practical examples and best practices that will be highly beneficial to the Python community, demonstrating the language’s power and potential in real-world applications.

As an Outreachy alum, at the end of this talk, I will share my personal journey in open source and insights into how such programs can catalyze professional growth and drive meaningful contributions to the tech industry. Whether you’re a seasoned developer, a machine learning enthusiast, or someone passionate about open source, this talk promises to be a blend of technical depth, innovative thinking, and inspiring experiences.

Notes

No specific technical requirements. The basic necessities such as an HDMI connection, microphone, and reliable internet connection will suffice. The talk will feature a presentation and a few short demos of real-time actions performed by the models developed by the Bugbug community.

I am an Outreachy alum and have worked on Mozilla’s Bugbug project, an open-source initiative that leverages Python and machine learning to revolutionize software engineering practices. With a Bachelor’s degree in Software Engineering and experience in applying AI to software development, I possess a solid understanding of both the technical and practical aspects of this field.

Currently, I am a volunteer at Mozilla, where my work involves hands-on development and research, allowing me to provide unique insights into the implementation and benefits of Python, Machine Learning and Open source . Additionally, as an Outreachy alum, I have a compelling story to share about how Outreachy opened doors to Mozilla, leading to exciting new opportunities. (A special reveal will be made during the talk.)

I am passionate about sharing knowledge and contributing to the Python community. My talk, which I am preparing with the help of Mozilla employees and fellow researchers, will offer valuable, actionable insights for developers and enthusiasts alike. My goal is to break down complex concepts into understandable and engaging content, encouraging new contributors to Bugbug and demonstrating the practical applications of Python in the industry.

Last year, as an attendee at Pycon UG 23, I was deeply impressed. This year, I aim to share my knowledge and give back to the Python community. I believe my talk will provide valuable insights for developers and enthusiasts of all career levels.