Toward improved artificial intelligence in requirements engineering: Metadata for tracing datasets

Jane Huffman Hayes, Jared Payne, Mallory Leppelmeier

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Scopus citations

Abstract

Data is the driver of artificial intelligence in requirements engineering. While some applications may lend themselves to training sets that are easily accessible (such as sentiment detection, feature request classification, requirements prioritization), other tasks face data challenges. Tracing and domain model building are examples of applications where data is not easily found or in the proper format or with the necessary metadata to support deep learning, machine learning, or other artificial intelligence techniques. This paper surveys datasets available from sources such as the Center of Excellence for Software and Systems Traceability and provides valuable metadata that can be used by re-searchers or practitioners when deciding what datasets to use, what aspects of datasets to use, what features to use in deep learning, and more.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE 27th International Requirements Engineering Conference Workshops, REW 2019
Pages256-262
Number of pages7
ISBN (Electronic)9781728151656
DOIs
StatePublished - Sep 2019
Event27th IEEE International Requirements Engineering Conference Workshops, REW 2019 - Jeju Island, Korea, Republic of
Duration: Sep 23 2019Sep 27 2019

Publication series

NameProceedings - 2019 IEEE 27th International Requirements Engineering Conference Workshops, REW 2019

Conference

Conference27th IEEE International Requirements Engineering Conference Workshops, REW 2019
Country/TerritoryKorea, Republic of
CityJeju Island
Period9/23/199/27/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

  • Artificial intelligence
  • Datasets
  • Deep learning
  • Machine learning
  • Metadata
  • Requirement engineering
  • Training sets

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software
  • Safety, Risk, Reliability and Quality
  • Artificial Intelligence

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