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AutoStyle: Scale-driven Hint Generation for Coding Style
| Content Provider | Semantic Scholar |
|---|---|
| Author | Choudhury, Rohan Roy Yin, Hezheng Moghadam, Joseph Bahman Chen, Antares Fox, Armando |
| Copyright Year | 2016 |
| Abstract | While the use of autograders for code correctness is widespread, less e↵ort 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 statisticallysignificant greater improvement in code style than students in the control group. We also present experiments to demonstrate the e cacy and relevance of each of the di↵erent types of hints generated by our system. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.eecs.berkeley.edu/Pubs/TechRpts/2016/EECS-2016-40.pdf |
| Alternate Webpage(s) | https://www2.eecs.berkeley.edu/Pubs/TechRpts/2016/EECS-2016-40.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
