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. On the performance of method-level bug prediction: A negative result

    Research output: Contribution to journalArticleScientificpeer-review

  2. The effects of change decomposition on code review—a controlled experiment

    Research output: Contribution to journalArticleScientificpeer-review

  3. PathMiner: A library for mining of path-based representations of code

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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  1. CSCW 2015 ACM SIGCHI Best Paper Award

    Prize: Prize (including medals and awards)

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