Machine Learning Methods in Software Engineering

Our current areas of interest:

• Detecting defects in object-oriented architecture and automatic recommendation of appropriate refactorings that optimize code structure.
• Code clones detection and 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 dynamics of developers’ coding style.
• Utilizing historical data to augment collaboration tools, e.g. through recommender systems.
• Anomaly detection on 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 methods to change, bugs location, 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.