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

Chong Wang; Peng Liang; Maya Daneva; Marten van Sinderen

doi:10.18293/SEKE2019-176

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

Chong Wang, Peng Liang, Maya Daneva, Marten van Sinderen

Semantics, Cybersecurity & Services

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

9 Citations (Scopus)

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 language	English
Title of host publication	Proceedings - SEKE 2019
Subtitle of host publication	31st International Conference on Software Engineering and Knowledge Engineering
Publisher	Knowledge Systems Institute Graduate School
Pages	398-403
Number of pages	6
ISBN (Electronic)	1891706489
DOIs	https://doi.org/10.18293/SEKE2019-176
Publication status	Published - 1 Jan 2019
Event	31st International Conference on Software Engineering and Knowledge Engineering, SEKE 2019 - Lisbon, Portugal Duration: 10 Jul 2019 → 12 Jul 2019 Conference number: 31

Publication series

Name	Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE
Number	31
Volume	2019
ISSN (Print)	2325-9000
ISSN (Electronic)	2325-9086

Conference

Conference	31st International Conference on Software Engineering and Knowledge Engineering, SEKE 2019
Abbreviated title	SEKE
Country/Territory	Portugal
City	Lisbon
Period	10/07/19 → 12/07/19

Keywords

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

Access to Document

10.18293/SEKE2019-176

Cite this

Wang, C., 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

Wang, Chong ; Liang, Peng ; Daneva, Maya et al. / Augmenting app reviews with app changelogs : An approach for app reviews classification. Proceedings - SEKE 2019: 31st International Conference on Software Engineering and Knowledge Engineering. Knowledge Systems Institute Graduate School, 2019. pp. 398-403 (Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE; 31).

@inproceedings{1c252c29692545ae992db32d15305f0b,

title = "Augmenting app reviews with app changelogs: An approach for app reviews classification",

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.",

keywords = "App changelogs, App reviews, Data-driven requirements engineering, Machine learning, Requirements analysis",

author = "Chong Wang and Peng Liang and Maya Daneva and {van Sinderen}, Marten",

year = "2019",

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publisher = "Knowledge Systems Institute Graduate School",

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Wang, C, 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. Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE, no. 31, vol. 2019, Knowledge Systems Institute Graduate School, pp. 398-403, 31st International Conference on Software Engineering and Knowledge Engineering, SEKE 2019, Lisbon, Portugal, 10/07/19. https://doi.org/10.18293/SEKE2019-176

Augmenting app reviews with app changelogs: An approach for app reviews classification. / Wang, Chong; Liang, Peng; Daneva, Maya et al.
Proceedings - SEKE 2019: 31st International Conference on Software Engineering and Knowledge Engineering. Knowledge Systems Institute Graduate School, 2019. p. 398-403 (Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE; Vol. 2019, No. 31).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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N1 - Conference code: 31

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N2 - 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.

AB - 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.

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Wang C, Liang P, Daneva M , van Sinderen M. 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. Knowledge Systems Institute Graduate School. 2019. p. 398-403. (Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE; 31). doi: 10.18293/SEKE2019-176

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

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