1 / 9

Detection of Programming Difficulties

Explore a framework for classifying and addressing programming difficulties through the development of a recommendation system. Capture user code, browser searches, and keywords to provide tailored assistance. Learn Eclipse plugin and Chrome extension development, database management, and machine learning for classification.

lplatt
Download Presentation

Detection of Programming Difficulties

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Detection of Programming Difficulties Nils Erik Persson University of North Carolina at Chapel Hill npersson@cs.unc.edu Advisor: Prasun Dewan

  2. Motivation • As programmers, we experience difficulty constantly • We seek assistance from the internet, colleagues, superiors, etc. • Search time and energy is a barrier to all of these

  3. Vision • Create a difficulty recommendation system that can: • Detect user difficulty • Determine the current task • Provide reasonable assistance

  4. Project Goal • Develop the framework to support classification and assistance

  5. Task Overview • Capture all words in user’s code • Determine keywords • Capture searches in browser logs • Store/update keyword -> search mappings in a database • Recommend searches based on keywords to the user

  6. What You’ll Learn • Eclipse plugin development • Chrome extension development • Database creation and management • Distributed systems (centralized server with multiple clients) • Machine learning (classification)

  7. Classification Example • Count of words created from underlying code over time • Document states clustered based on the counts Cluster/ Topic Document state word counts

  8. Classification Example • Determine which words are important for each cluster • Look at relative weights of each word on classification • Label topics with these words!

  9. Questions?

More Related