识别编程计划的混合方法

A. Quilici
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引用次数: 9

摘要

大多数当前的程序理解模型不太可能成功地扩大规模。自顶向下的方法需要预先了解程序应该做什么,这在老化的软件系统中很少可用。自底向上方法需要将程序与编程计划库完全匹配,这对于理解包含许多领域特定计划的程序所需的大型计划库是不切实际的。本文提出了一种用于程序理解的混合方法,该方法使用计划库的索引分层组织来限制程序理解过程中考虑的候选计划的数量。这种方法是基于对学生程序员的观察,他们试图对面向几何的C函数进行自下而上的理解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A hybrid approach to recognizing programming plans
Most current models of program understanding are unlikely to scale up successfully. Top-down approaches require advance knowledge of what the program is supposed to do, which is rarely available with aging software systems. Bottom-up approaches require complete matching of the program against a library of programming plans, which is impractical with the large plan libraries needed to understand programs that contain many domain-specific plans. This paper presents a hybrid approach to program understanding that uses an indexed, hierarchical organization of the plan library to limit the number of candidate plans considered during program understanding. This approach is based on observations made from studying student programmers attempt to perform bottom-up understanding on geometrically-oriented C functions.<>
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