调查使用代码分析和NLP来促进标识符的一致使用

B. Lin, Simone Scalabrino, Andrea Mocci, R. Oliveto, G. Bavota, Michele Lanza
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引用次数: 22

摘要

无意义的标识符以及在源代码中不一致的标识符使用可能会妨碍代码的可读性,并导致软件维护工作的增加。在过去的几年中,人们一直致力于通过利用静态代码分析和自然语言处理(NLP)的方法,在系统的不同部分中促进标识符的一致使用。这些技术已经在小规模研究中进行了评估,但尚不清楚它们如何相互比较以及如何相互补充。此外,一个成熟的更大的经验评估仍然缺失。我们的目标是弥合这一差距。我们要求五个项目的开发人员评估由三种技术生成的建议的意义,其中两种技术已经存在于文献中(一种利用静态分析,一种使用NLP),另一种是我们提出的新技术。总共评估了922个重命名重构,据我们所知,这是评估和比较重命名重构工具促进标识符一致使用的最大的实证研究。我们的研究揭示了当前重命名重构推荐的最新技术,并指出了未来工作的方向。
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Investigating the Use of Code Analysis and NLP to Promote a Consistent Usage of Identifiers
Meaningless identifiers as well as inconsistent use of identifiers in the source code might hinder code readability and result in increased software maintenance efforts. Over the past years, effort has been devoted to promoting a consistent usage of identifiers across different parts of a system through approaches exploiting static code analysis and Natural Language Processing (NLP). These techniques have been evaluated in small-scale studies, but it is unclear how they compare to each other and how they complement each other. Furthermore, a full-fledged larger empirical evaluation is still missing.,,We aim at bridging this gap. We asked developers of five projects to assess the meaningfulness of the recommendations generated by three techniques, two already existing in the literature (one exploiting static analysis, one using NLP) and a novel one we propose. With a total of 922 rename refactorings evaluated, this is, to the best of our knowledge, the largest empirical study conducted to assess and compare rename refactoring tools promoting a consistent use of identifiers. Our study sheds light on the current state-of-the-art in rename refactoring recommenders, and indicates directions for future work.
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