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Computer Science > Software Engineering

arXiv:2408.02557 (cs)
[Submitted on 5 Aug 2024 (v1) , last revised 25 Apr 2025 (this version, v2)]

Title: AutoFL: A Tool for Automatic Multi-granular Labelling of Software Repositories

Title: AutoFL:用于软件仓库自动多粒度标注的工具

Authors:Cezar Sas, Andrea Capiluppi
Abstract: Software comprehension, especially of new code bases, is time consuming for developers, especially in large projects with multiple functionalities spanning various domains. One strategy to reduce this effort involves annotating files with meaningful labels that describe the functionalities contained. However, prior research has so far focused on classifying the whole project using README files as a proxy, resulting in little information gained for the developers. Our objective is to streamline the labelling of files with the correct application domains using source code as input. To achieve this, in prior work, we evaluated the ability to annotate files automatically using a weak labelling approach. This paper presents AutoFL, a tool for automatically labelling software repositories from source code. AutoFL allows multi-granular annotations including: \textit{file}, \textit{package}, and \textit{project} -level. We provide an overview of the tool's internals, present an example analysis for which AutoFL can be used, and discuss limitations and future work.
Abstract: 软件理解,尤其是对新代码库的理解,对于开发人员来说是耗时的,尤其是在涉及多个功能并跨越不同领域的大型项目中。 一种减少这种努力的策略是使用描述包含功能的有意义标签来标注文件。 然而,之前的研究主要集中在使用README文件作为代理来分类整个项目,这为开发人员带来的信息很少。 我们的目标是使用源代码作为输入,简化文件的正确应用领域标注。 为了实现这一点,在先前的工作中,我们评估了使用弱标注方法自动标注文件的能力。 本文介绍了AutoFL,一个从源代码自动标注软件仓库的工具。 AutoFL允许多粒度标注,包括:\textit{文件}、\textit{包}和\textit{项目}-级别。 我们提供了该工具内部结构的概述,展示了AutoFL可以使用的示例分析,并讨论了局限性和未来工作。
Subjects: Software Engineering (cs.SE)
Cite as: arXiv:2408.02557 [cs.SE]
  (or arXiv:2408.02557v2 [cs.SE] for this version)
  https://doi.org/10.48550/arXiv.2408.02557
arXiv-issued DOI via DataCite

Submission history

From: Cezar Sas [view email]
[v1] Mon, 5 Aug 2024 15:34:26 UTC (1,340 KB)
[v2] Fri, 25 Apr 2025 15:08:51 UTC (1,352 KB)
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