Feature Extraction from Japanese Natural Language Requirements Documents for Software Product Line Engineering

Kenji Hisazumi, Yuedong Xiao, Akira Fukuda

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Analyzing and extracting features from requirement specifications is an indispensable activity to support Software Product Line Engineering. However, performing features extraction is a time-consuming and inefficient task, since massive textual requirements need to be analyzed and classified. Most of the current approaches exhibited limitations: hindered applicability with requirements in Japanese; the support tools proposed were not made available publicly and thus making it hard for practitioners' adoption. This paper proposes a feature extraction approach from requirement specifications in Japanese using natural language processing techniques. Also, we propose a ranking method for extracted features to reduce efforts reviewing feature candidates. A case study was conducted to evaluate the performance of the proposed approach. Initial results show that 90.7% features were extracted correctly, and the top 40% features extracted contained 79.1% true features.

Original languageEnglish
Title of host publicationProceedings - Companion of the 19th IEEE International Conference on Software Quality, Reliability and Security, QRS-C 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages322-329
Number of pages8
ISBN (Electronic)9781728139258
DOIs
Publication statusPublished - Jul 2019
Event19th IEEE International Conference on Software Quality, Reliability and Security Companion, QRS-C 2019 - Sofia, Bulgaria
Duration: Jul 22 2019Jul 26 2019

Publication series

NameProceedings - Companion of the 19th IEEE International Conference on Software Quality, Reliability and Security, QRS-C 2019

Conference

Conference19th IEEE International Conference on Software Quality, Reliability and Security Companion, QRS-C 2019
CountryBulgaria
CitySofia
Period7/22/197/26/19

All Science Journal Classification (ASJC) codes

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

Fingerprint Dive into the research topics of 'Feature Extraction from Japanese Natural Language Requirements Documents for Software Product Line Engineering'. Together they form a unique fingerprint.

Cite this