分析自然语言处理对模型中特征定位的影响

Proceedings of the 16th ACM SIGPLAN International Conference on Generative Programming: Concepts and Experiences Pub Date : 2017-10-23 DOI:10.1145/3136040.3136052

Raúl Lapeña, Jaime Font, Ó. Pastor, Carlos Cetina

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引用次数: 11

摘要

特征定位(Feature Location, FL)是软件工程领域，特别是软件产品的维护和开发中的一项常见任务。FL的结果在很大程度上取决于编写特性描述和软件工件的风格。因此，自然语言处理(NLP)技术被用于处理它们。通过本文，我们通过潜在语义索引分析了最常见的NLP技术对概念模型中FL的影响，以及在嵌入领域知识的过程中人类参与的影响。我们在铁路车辆领域的实际工业案例研究中评估了这些技术。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Analyzing the impact of natural language processing over feature location in models

Feature Location (FL) is a common task in the Software Engineering field, specially in maintenance and evolution of software products. The results of FL depend in a great manner in the style in which Feature Descriptions and software artifacts are written. Therefore, Natural Language Processing (NLP) techniques are used to process them. Through this paper, we analyze the influence of the most common NLP techniques over FL in Conceptual Models through Latent Semantic Indexing, and the influence of human participation when embedding domain knowledge in the process. We evaluated the techniques in a real-world industrial case study in the rolling stocks domain.

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Proceedings of the 16th ACM SIGPLAN International Conference on Generative Programming: Concepts and Experiences

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