Rohan Roy Choudhury and Hezheng Yin and Joseph Moghadam and Antares Chen and Armando Fox

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2016-40

May 5, 2016

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2016/EECS-2016-40.pdf

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.

Advisors: Armando Fox

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BibTeX citation:

@mastersthesis{Roy Choudhury:EECS-2016-40,
    Author= {Roy Choudhury, Rohan and Yin, Hezheng and Moghadam, Joseph and Chen, Antares and Fox, Armando},
    Title= {AutoStyle: Scale-driven Hint Generation for Coding Style},
    School= {EECS Department, University of California, Berkeley},
    Year= {2016},
    Month= {May},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2016/EECS-2016-40.html},
    Number= {UCB/EECS-2016-40},
    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.},
}

EndNote citation:

%0 Thesis
%A Roy Choudhury, Rohan 
%A Yin, Hezheng 
%A Moghadam, Joseph 
%A Chen, Antares 
%A Fox, Armando 
%T AutoStyle: Scale-driven Hint Generation for Coding Style
%I EECS Department, University of California, Berkeley
%D 2016
%8 May 5
%@ UCB/EECS-2016-40
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2016/EECS-2016-40.html
%F Roy Choudhury:EECS-2016-40