During the course of research, we have developed and released the following software.

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Trustworthy Machine Learning

• ActiveThief: Model extraction using active learning and unannotated public data
  For technical details, see our AAAI'20 paper.

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Machine Learning for Software Engieering

• CuBERT: Learning and evaluating contextual embedding of source code
  For technical details, see our ICML'20 paper.
• ETHPy150Open dataset: A redistributable subset of the ETH Py150 corpus
  For technical details, see our ICML'20 paper.
• NBL: Neural-attribution for semantic bug localization
  For technical details, see our NeurIPS'19 paper.
• RLAssist: Deep reinforcement learning for syntactic error repair
  For technical details, see our AAAI'19 paper.
• Bandit AFL: Greybox fuzzing as a contextual bandits problem
  For technical details, see the technical report.
• DeepFix: Fixing common C language errors by deep learning
  For technical details, see our AAAI'17 paper.
• CoderAssist: Semi-supervised verified feedback generation for programming assignments 
  For technical details, see our FSE'16 paper.
• MintHint: Automated synthesis of repair hints 
  For technical details, see our ICSE'14 paper.
• MathFinder: Math API discovery and migration 
  For technical details, see our FASE'13 and TOSEM'14 papers.

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Concurrency and Distribution

• Symbolic simulation and test generation for Matlab Simulink/Stateflow 
  For technical details, see our EMSOFT'08 and CAV'09 papers.
• SPOTS: A system for proving optimizing transformations sound 
  For technical details, see our SEFM'06, ENTCS'07 and SPE'09 papers.