Machine Learning for Software Development Andreas Zeller + Tural Mammadov


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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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