04/2024: Attended a fantastic Dagstuhl Seminar on Code Search!
02/2024: Successfully defended my PhD (summa cum laude) and starting a new position at USI advised by Prof. Mauro Pezzè in Switzerland.
01/2024: Start serving as a reviewer for the prestigious journal IEEE Transactions on Software Engineering (IEEE TSE).
12/2023: ICSE 2024! Our paper: “PyTy: Repairing Static Type Errors in Python” has been accepted for the ICSE 2024 conference in Portugal.
09/2023: ASE! Our paper: “DiffSearch: A Scalable and Precise Search Engine for Code Changes” has been accepted for the Journal-first track at ASE 2023 in Luxembourg.
06/2023: Won a Uber competition on Generative AI for developer productivity among 103 teams worldwide.
05/2023: Generative AI at Uber! I am joining Uber for a research internship in Amsterdam for summer 2023.
04/2023: Dagstuhl Seminar! I was invited to the world’s leading researchers Dagstuhl Seminar about Code Search for spring 2024.
03/2023: I was invited by JetBrains in their Munich office to discuss our paper “DiffSearch: A Scalable and Precise Search Engine for Code Changes”.
02/2023: Start serving as a reviewer for the prestigious journal ACM Transactions on Software Engineering and Methodology (TOSEM).
12/2022: Distinguished Paper Award! Our paper “The Evolution of Type Annotations in Python: An Empirical Study” received an ACM SIGSOFT Distinguished Paper Award at ESEC/FSE 2022 in Singapore.
11/2022: IEEE TSE! Our paper: “DiffSearch: A Scalable and Precise Search Engine for Code Changes” has been accepted for the IEEE Transactions on Software Engineering journal.
10/2022: ACM CSUR! Our paper: “Code Search: A Survey of Techniques for Finding Code” has been accepted for the ACM Computing Surveys journal.
09/2022: ESEC/FSE! Our paper: “The Evolution of Type Annotations in Python: An Empirical Study” has been accepted for the ESEC/FSE 2022 conference.
05/2022: ICSE SRC 2022! Winner of the second prize ($300) for the ICSE ACM Student Research Competition (SRC) with the submission “Efficiently and Precisely Searching for Code Changes with DiffSearch”.
PyTy is a novel automated technique aimed at fixing type errors in Python. It was developed based on a study and employs a dataset named PyTyDefects, containing over 2,700 Python type errors fixes. The paper highlights the use of cross-lingual transfer learning to enhance PyTy's effectiveness, even with a small dataset. This involves adapting an existing program repair model for PyTy's use. PyTy proved highly effective, successfully resolving 85.4% of type errors in the evaluation. Additionally, its real-world applicability is shown by the high acceptance rate of GitHub pull requests using PyTy's fixes.