Automatic Generation of Fill-in-the-Blank Programming Problems
Automatic Generation of Fill-in-the-Blank Programming Problems
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DOI：
10.1109/mcsoc.2019.00034
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发表时间：
2019-10
期刊：
影响因子：
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通讯作者：
Kenta Terada;Y. Watanobe
中科院分区：
文献类型：
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作者：
Kenta Terada;Y. Watanobe
在解决编程问题时，初学者很难从头开始编写程序代码。克服这一困难的一种方法是向他们提供一个采用填充式格式的编程问题。在这项工作中，我们提出了一种自动生成编程问题的方法，该方法有两个关键组成部分，即选择示例性源代码和选择空白位置。在选择典型源代码方面，提出了在在线评判系统(OJ)中采用轮廓分析的k-均值聚类。针对空白点的选择问题，提出了一种基于序贯条件随机场(CRF)的双向长短期记忆网络模型。我们在如何产生填空编程问题的背景下讨论了对所提出的方法的评估。
In solving programming problems, it is difficult for beginners to create program code from scratch. One way to navigate this difficulty is to provide a programming problem to them which takes a fill-in-the-blank format. In this work, we propose a method to automatically generate programming problems that has two key constituents, selection of exemplary source code and selection of places to be blanks. In terms of selecting exemplary source code, k-means clustering with silhouette analysis in the Online Judge System (OJ) is proposed. Regarding the selection of places to be blanks, a model based on a bidirectional Long Short-Term Memory Network (Bi-LSTM) with a sequential Conditional Random Field (CRF) is proposed. We discuss evaluation of the proposed approach in the context of how fill-in-the-blank programming problems are generated.