Research group

Machine Learning Methods in Software Engineering

Our current area of interest:

  • Detecting defects in object-oriented architecture and automatic recommendation of appropriate refactorings that optimize code structure.
  • Anomaly detection for Kotlin compiler and programs in Kotlin.
  • Analysis of code structure and style for plagiarism detection, finding programmers with similar code styles etc.
  • Analysis of developers' code style changes within collaborative projects, how code reviews affect developers' coding style and habits, etc.
  • Code clones detection and tools for automatic detection and extraction of reusable code fragments.
  • Analysis of mistakes students make solving programming tasks and development of tools to predict types of mistakes made and recommendations on how to fix them.
  • Knowledge modeling for students that learn programming and adaptation of course content based on their progress.
  • Automated code generation from natural language descriptions, API calls used, etc.
  • User intent and context analysis for smarter autocompletion and documentation suggestions.
  • Commit-based analysis of code repositories predicting methods to change, bugs location and other events.
  • Methods for automated bug detection.

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