Automatic Generation of Fill-in-the-Blank Programming Problems

Abstract

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.