Complexitor:一个实用的学习算法时间复杂度的教育工具

ComTech Pub Date : 2017-03-31 DOI:10.21512/COMTECH.V8I1.3783
E. Elvina, Oscar Karnalim
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引用次数: 15

摘要

基于非正式调查,学习算法的时间复杂性在理论上可能相当难以理解。因此,本研究提出了Complexitor,一种以实用的方式学习算法时间复杂性的教育工具。学生可以学习如何通过算法实现的实际执行来确定算法的时间复杂性。他们只需要提供算法实现(即用特定编程语言编写的源代码)和测试用例来学习时间复杂性。输入后,Complexitor根据测试用例生成执行序列,并通过Pearson相关性确定其时间复杂度。将一个与执行序列相关值最高的算法时间复杂度作为其结果。基于评估,可以得出结论,只要给定输入集的分布是平衡的,该机制对于确定时间复杂性是非常有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Complexitor: An Educational Tool for Learning Algorithm Time Complexity in Practical Manner
Based on the informal survey, learning algorithm time complexity in a theoretical manner can be rather difficult to understand. Therefore, this research proposed Complexitor, an educational tool for learning algorithm time complexity in a practical manner. Students could learn how to determine algorithm time complexity through the actual execution of algorithm implementation. They were only required to provide algorithm implementation (i.e. source code written on a particularprogramming language) and test cases to learn time complexity. After input was given, Complexitor generated execution sequence based on test cases and determine its time complexity through Pearson correlation. An algorithm time complexity with the highest correlation value toward execution sequence was assigned as its result. Based on the evaluation, it can be concluded this mechanism is quite effective for determining time complexity as long as the distribution of given input set is balanced.
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来源期刊
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审稿时长
16 weeks
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