Machine Learning for Software Development Andreas Zeller + Tural Mammadov

News

17.11.2022

Deadline extension for Assignment 2

Dear students, 

The deadline for Assignment 2 is extended till November 30th. 

A short reminder: the seminar session on the 23rd of November is canceled.

Best,

Andreas and Tural

10.11.2022

Assignment 1

Dear Students,

You have to prepare a summary of "Using Reinforcement Learning for Load Testing of Video Games" research paper as your first assignment. You can find a link to the corresponding paper in the materials section. Please hand in your summary via the... Read more

Dear Students,

You have to prepare a summary of "Using Reinforcement Learning for Load Testing of Video Games" research paper as your first assignment. You can find a link to the corresponding paper in the materials section. Please hand in your summary via the CMS system. The submission deadline for assignment 1 is 16.11.2022, 23:59.

Best,

Andreas and Tural

08.11.2022

Next meeting

Dear Students,

We have set up the seminar timetable and populated the material section with research papers.

The next seminar meeting will take place on Wednesday 9th of November at 16:15, and you should not prepare anything for this session.

Best,

Andreas and Tural

 

Machine Learning for Software Development

Description: Wouldn't it be cool if one could teach computers to help developers in coding, testing, and debugging software? In this seminar, we will discuss current results and new problems by applying techniques from machine learning into software development, based on relevant scientific papers. We will explore techniques such as

  • Generating software tests
  • Generating test oracles
  • Generating graphical user interfaces
  • Code search and code fill
  • Locating faults
  • Program repair using neural networks
  • and more!

The general process will be as follows: Each week, you get 1-2 reading assignments and write an abstract about them. You may also be asked to give an (ungraded) five-minute short presentation to kick off the discussion and improve your presentation skills. At the end of the seminar, you give a 15-20 minute presentation on one of the techniques, preferably including small experiments or demonstrations on how well they work; these will then be graded.

Requirements: Prior knowledge in machine learning and software engineering will be beneficial. For experiments and evaluations, programming knowledge will be helpful, too. We recommend doing experiments and evaluations in Jupyter Notebooks – so don't be afraid of them.

Registration: To register for this seminar, use the SIC Seminar Registration Page.

 



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