Can Machine Learning Facilitate Remote Pair Programming? Challenges, Insights & Implications
Remote pair programming encapsulates the benefits of well-researched (co-located) pair programming. However, its effectiveness is hindered by challenges including pair incompatibility, imbalanced roles, and inclinations to work alone. Recent research has explored pedagogical methods to alleviate these challenges but none have considered the integration of machine learning agents to facilitate remote pair programming. Therefore, we investigated the capabilities of popular text classification algorithms on identifying three facets of pair programming: dialogue acts, creativity stages, and pair programming roles. We collected a dataset of 3,444 utterances from a lab study of 18 pair programmers in a simulated remote environment. We found that pair programming dialogue poses a challenge as it is often unpremeditated and inadequately structured, despite this the accuracy of machine learning algorithms was improved by the choice of contextual dialogue features. Our results have implications for facilitating pair programming in global software development and online computer science education.
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Jack WilliamsMicrosoft Research, Carina NegreanuMicrosoft Research, Andrew D. GordonMicrosoft Research and University of Edinburgh, Advait SarkarMicrosoft Research and University of CambridgeAuthorizer link
|Can Machine Learning Facilitate Remote Pair Programming? Challenges, Insights & ImplicationsFull paper|
Peter RobeThe University of Tulsa, Sandeep KuttalThe University of Tulsa, Yunfeng ZhangIBM T.J. Watson Research Center, Rachel BellamyIBM T.J. Watson Research CenterAuthorizer link