Research group

Machine Learning Methods in Software Engineering

Our current areas of interest:

  • Detecting defects in object-oriented architecture and automatic suggestion of appropriate refactorings that optimize code structure.
  • Detecting code clones and creating tools for automatic detection and extraction of reusable code fragments.
  • Building richer embeddings of code for plagiarism detection, method and variable name prediction and code summarization.
  • Analysis of developers’ coding style dynamics.
  • Utilizing historical data to augment collaboration tools, e.g. through recommender systems.
  • Anomaly detection in code.
  • Automated code generation from natural language descriptions, API calls used, etc.
  • Automated coding assistance both for students and seasoned developers, including finding/fixing typical errors, IDE feature discovery and adoption, user intent and context analysis.
  • Commit-based analysis of code repositories predicting changes, locations of bugs, and other events.
  • Methods for automated bug detection and program repair.

If you are interested in working on these topics with us, please contact Timofey Bryksin.