Augmenting app reviews with app changelogs: An approach for app reviews classification

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Abstract

Recent research on the automatic classification of app reviews either focused on grouping app reviews into categories relevant to software evolution, or employed app reviews as the only research data to improve app reviews classification. Although it was reported that app review classification can benefit from supplementing user reviews with the data from other sources, only a few studies employed app changelogs for this purpose. This paper explores how to augment app reviews with changelogs to improve the accuracy and performance of classifying functional and non-functional requirements in app reviews. Specifically, we propose AUG-AC as an approach to extract feature words from app changelogs and construct the augments for app reviews. Next, we designed a series of experiments to evaluate our approach, varying in the length of AC-based augments for app reviews. The results show that AUG-AC outperforms the existing method by using app changelogs as a source of data next to app reviews.

Original languageEnglish
Title of host publicationProceedings - SEKE 2019
Subtitle of host publication31st International Conference on Software Engineering and Knowledge Engineering
PublisherKnowledge Systems Institute Graduate School
Pages398-403
Number of pages6
ISBN (Electronic)1891706489
DOIs
Publication statusPublished - 1 Jan 2019

Publication series

NameProceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE
Number31
Volume2019
ISSN (Print)2325-9000
ISSN (Electronic)2325-9086

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Keywords

  • App changelogs
  • App reviews
  • Data-driven requirements engineering
  • Machine learning
  • Requirements analysis

Cite this

Wang, C., Wang, T., Liang, P., Daneva, M., & van Sinderen, M. (2019). Augmenting app reviews with app changelogs: An approach for app reviews classification. In Proceedings - SEKE 2019: 31st International Conference on Software Engineering and Knowledge Engineering (pp. 398-403). (Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE; Vol. 2019, No. 31). Knowledge Systems Institute Graduate School. https://doi.org/10.18293/SEKE2019-176