CoolTeD: A Web-based Collaborative Labeling Tool for the Textual Dataset

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Abstract

High-quality labeled textual data are vital for automatic mining and analysis of massive textual data produced by software systems. Several tools have been designed to facilitate manual labeling of textual data on different levels of granularity. However, these tools neither aim to provide statistics and analysis of labeled textual data, nor support collaboration among the coders to reduce the time cost in manual labeling and enhance the quality of labeling results. In this paper, we developed a Web-based labeling tool named CoolTeD (available at http://williamsriver.cn) for collaborative labeling of the textual datasets. Specifically, CoolTeD can be used: (1) to label textual data from the perspective of requirements types based on ISO 25010, (2) to review the labeling results with different confidence levels and contradictory labels, (3) to automatically calculate Cohen's Kappa coefficient of multiple coders, and (4) to visualize the labeling results. The tool demo is available at https://youtu.be/xVkrB_Cs1J8

Original languageEnglish
Title of host publication2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)
PublisherIEEE
Pages613-617
Number of pages5
ISBN (Electronic)978-1-6654-3786-8
ISBN (Print)978-1-6654-3787-5
DOIs
Publication statusPublished - 21 Jul 2022
Event29th IEEE International Conference on Software Analysis, Evolution and Reengineering, SANER 2022 - Virtual
Duration: 15 Mar 202218 Mar 2022
Conference number: 29

Publication series

NameProceedings - 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering, SANER 2022

Conference

Conference29th IEEE International Conference on Software Analysis, Evolution and Reengineering, SANER 2022
Abbreviated titleSANER
Period15/03/2218/03/22

Keywords

  • Collaborative Labeling
  • Data Labeling
  • Data Visualization
  • Requirements Engineering
  • Textual Data
  • 2024 OA procedure

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