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摘要翻译：
本文提出了一种自动为程序设计问题提供反馈的新方法。为了使用这种方法，我们需要一个作业的参考实现，以及一个由学生可能犯的错误的潜在修正组成的错误模型。使用这些信息，系统会自动对学生的错误解决方案进行最小的修正，为他们提供一个给定解决方案到底有多不正确的量化度量，以及关于他们做错了什么的反馈。本文介绍了一种用修正规则描述错误模型的简单语言，并形式化地定义了一种规则指导的翻译策略，该策略将在错误程序中寻找最小修正量的问题简化为从草图中合成正确程序的问题。我们已经对我们的系统进行了评估，在6.00和6.00x中获得了数千个真实的学生尝试。我们的结果表明，相对简单的错误模型平均可以纠正65%的错误提交。
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英文标题：
《Automated Feedback Generation for Introductory Programming Assignments》
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作者：
Rishabh Singh, Sumit Gulwani and Armando Solar-Lezama
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最新提交年份：
2012
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Programming Languages 程序设计语言
分类描述：Covers programming language semantics, language features, programming approaches (such as object-oriented programming, functional programming, logic programming). Also includes material on compilers oriented towards programming languages; other material on compilers may be more appropriate in Architecture (AR). Roughly includes material in ACM Subject Classes D.1 and D.3.
涵盖程序设计语言语义，语言特性，程序设计方法（如面向对象程序设计，函数式程序设计，逻辑程序设计）。还包括面向编程语言的编译器的材料；关于编译器的其他材料可能在体系结构(AR)中更合适。大致包括ACM主题课程D.1和D.3中的材料。
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一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
We present a new method for automatically providing feedback for introductory programming problems. In order to use this method, we need a reference implementation of the assignment, and an error model consisting of potential corrections to errors that students might make. Using this information, the system automatically derives minimal corrections to student's incorrect solutions, providing them with a quantifiable measure of exactly how incorrect a given solution was, as well as feedback about what they did wrong. We introduce a simple language for describing error models in terms of correction rules, and formally define a rule-directed translation strategy that reduces the problem of finding minimal corrections in an incorrect program to the problem of synthesizing a correct program from a sketch. We have evaluated our system on thousands of real student attempts obtained from 6.00 and 6.00x. Our results show that relatively simple error models can correct on average 65% of all incorrect submissions.
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PDF链接：
https://arxiv.org/pdf/1204.1751
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