Research interests

My research field is empirical software engineering. My current vision is to support software peer code review, developing a deep scientific understanding of this process to move code review away from decisions based on intuition, or activities painstakingly conducted manually, into solutions created using data-driven mathematical models, which exploit the large amount of information available during the software engineering and review process.

My research has advanced the fundamentals of software analytics, especially to mine unstructured software data, and the peer code review process. The techniques and methods I develop and use are at the intersection
of software engineering, information retrieval, data mining, machine learning, and social science.

Research output

  1. When Testing Meets Code Review: Why and How Developers Review Tests

    Research output: Scientific - peer-reviewConference contribution

  2. Investigating Type Declaration Mismatches in Python

    Research output: Scientific - peer-reviewConference contribution

  3. Re-evaluating Method-Level Bug Prediction

    Research output: Scientific - peer-reviewConference contribution

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Prizes

  1. CSCW 2015 ACM SIGCHI Best Paper Award

    Prize: Prize (including medals and awards)

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