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AutoStyle: Scale-driven Hint Generation for Coding Style

Fox, Armando
Choudhury, Rohan Roy
Yin, Hezheng
Moghadam, Joseph
Chen, Antares
Technical Report Identifier: EECS-2016-40
May 5, 2016

Abstract: While the use of autograders for code correctness is widespread, less effort has focused on automating feedback for good programming style: the tasteful use of language features and idioms to produce code that is not only correct, but also concise, elegant, and revealing of design intent. We present a system that can provide real-time actionable code style feedback to students in large introductory computer science classes. We demonstrate that in a randomized controlled trial, 70% of students using our system achieved the best style solution to a coding problem in less than an hour, while only 13% of students in the control group achieved the same. Students using our system also showed a statistically-significant greater improvement in code style than students in the control group. We also present experiments to demonstrate the efficacy and relevance of each of the different types of hints generated by our system.