FSE 2016 Workshops
24th ACM SIGSOFT International Symposium on the Foundations of Software Engineering (FSE 2016)
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2nd International Code Hunt Workshop on Educational Software Engineering (CHESE 2016), November 14, 2016, Seattle, WA, USA

CHESE 2016 – Proceedings

Contents - Abstracts - Authors
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2nd International Code Hunt Workshop on Educational Software Engineering (CHESE 2016)

Frontmatter

Title Page

Message from the Chairs
Welcome to the Second International Code Hunt Workshop on Educational Software Engineering (CHESE 2016), which will take place in Seattle, Washington, USA on Monday November 14, 2016. CHESE 2016 is a workshop of the 24th ACM SIGSOFT International Symposium on the Foundations of Software Engineering (FSE 2016). CHESE 2016 aims to build up a specific research community of educational software engineering with an emphasis on testing and game-based learning. In essence, this workshop is about “coding, testing, and education.”

Papers

Automatic Programming Error Class Identification with Code Plagiarism-Based Clustering
Sébastien Combéfis and Arnaud Schils
(École Centrale des Arts et Métiers, Belgium; Université Catholique de Louvain, Belgium)
Online platforms to learn programming are very popular nowadays. These platforms must automatically assess codes submitted by the learners and must provide good quality feedbacks in order to support their learning. Classical techniques to produce useful feedbacks include using unit testing frameworks to perform systematic functional tests of the submitted codes or using code quality assessment tools. This paper explores how to automatically identify error classes by clustering a set of submitted codes, using code plagiarism detection tools to measure the similarity between the codes. The proposed approach and analysis framework are presented in the paper, along with a first experiment using the Code Hunt dataset.
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Preliminary Analysis of Code Hunt Data Set from a Contest
Pierre McCauley, Brandon Nsiah-Ababio, Joshua Reed, Faramola Isiaka, and Tao Xie
(University of Illinois at Urbana-Champaign, USA)
Code Hunt (https://www.codehunt.com/) from Microsoft Research is a web-based serious gaming platform being popularly used for various programming contests. In this paper, we demonstrate preliminary statistical analysis of a Code Hunt data set that contains the programs written by students (only) worldwide during a contest over 48 hours. There are 259 users, 24 puzzles (organized into 4 sectors), and about 13,000 programs submitted by these users. Our analysis results can help improve the creation of puzzles in a future contest.
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