Assoc. Prof. Sherlock Licorish discusses using genetic improvement to enhance online code snippets.

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Sherlock Licorish, who is an Associate Professor in the Department of Information Science at the University of Otago, believes that online code snippets, such as those shared on Stack Overflow, should be reused with caution during software development. His research has demonstrated that such snippets can at times violate essential quality attributes, and may introduce defects if not properly adapted.

Recognising that many students and professional software developers reuse code snippets on a regular basis, Sherlock has been researching ways to identify noteworthy and relevant faults in online code and automatically improve such snippets before they are used. He recently gave a lightning talk on the possibility of using genetic improvement to enhance online code snippets at an event organised by the Never Work in Theory (NWIT) group. The NWIT is a group of academics and practitioners who aim to bridge the gap between academia and industry in the field of software engineering.

In his talk, Sherlock presented the results of his preliminary research, which demonstrated that genetic improvement can be used to enhance online code snippets. Sherlock hopes that his research can ultimately improve the reuse of code snippets in general, even those generated by large language models, such as ChatGPT.

Beyond source code analysis and repair, including crash and defect prediction, Sherlock’s recent research projects focus on identifying, grouping and ranking relevant information online and adapting machine learning approaches for addressing software engineering challenges and those in other domains. He also focusses on understanding how diverse individuals and teams contribute when they assemble to solve software engineering problems, where the findings of his research are informing his curriculum development work.

Should you like to learn more about Sherlock’s research please browse his university webpage here, or email him at