Social

Machine Learning for Software Engineering

Alex Bezzubov, Timofey Bryksin

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Abstract:

ML for Software Engineering Social brings together researchers working on applying ML to source code modeling and other software engineering (SE) tasks for building better developer tools.

We allocate some time at the beginning for short demos/presentations of ongoing work by different groups and spend the rest of the time on informal discussion on

  • What are the challenges of navigating research at the intersection of environments (academic and industrial research labs) and fields (ML, SE, PL)?

  • What are the frontiers of ML4SE research? What are the important unsolved problems in the area?

  • What are the biggest hurdles in creating a new ML for SE? (e.g. data, compute, etc)

  • Computer vision had ImageNet. NLP has [Super]GLUE. If it were up to you, what benchmark would you have the community work on?

  • What do the recent advances in NLP, such as Transformers, mean for the ML for SE area?

  • Common practical challenges of research results adoption in the industry.

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