AH-CID: a tool to automatically detect human-centric issues in app reviews

Collins Mathews; Kenny Ye; Jake Grozdanovski; Marcus Marinelli; Kai Zhong; Hourieh Khalajzadeh; Humphrey Obie; John Grundy

doi:10.5220/0010576503860397

AH-CID: a tool to automatically detect human-centric issues in app reviews

Collins Mathews, Kenny Ye, Jake Grozdanovski, Marcus Marinelli, Kai Zhong, Hourieh Khalajzadeh, Humphrey Obie, John Grundy

Department of Software Systems & Cybersecurity

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

3 Citations (Scopus)

Abstract

In modern software development, there is a growing emphasis on creating and designing around the end-user. This has sparked the widespread adoption of human-centred design and agile development. These concepts intersect during the user feedback stage in agile development, where user requirements are re-evaluated and utilised towards the next iteration of development. An issue arises when the amount of user feedback far exceeds the team’s capacity to extract meaningful data. As a result, many critical concerns and issues may fall through the cracks and remain unnoticed, or the team must spend a great deal of time in analysing the data that can be better spent elsewhere. In this paper, a tool is presented that analyses a large number of user reviews from 24 mobile apps. These are used to train a machine learning (ML) model to automatically generate the probability of the existence of human-centric issues, to automate and streamline the user feedback review analysis process. Evaluation shows an improved ability to find human-centric issues of the users.

Original language	English
Title of host publication	Proceedings of the 16th International Conference on Software Technologies
Editors	Hans-Georg Fill, Marten van Sinderen, Leszek Maciaszek
Publisher	Scitepress
Pages	386-397
Number of pages	12
ISBN (Electronic)	9789897585234
DOIs	https://doi.org/10.5220/0010576503860397
Publication status	Published - 2021
Event	International Conference on Software Technologies 2021 - Online Duration: 6 Jul 2021 → 8 Jul 2021 Conference number: 16th https://www.scitepress.org/ProceedingsDetails.aspx?ID=VnrgMHENH6Q=&t=1 (Proceedings)

Publication series

Name	Proceedings of the 16th International Conference on Software Technologies, ICSOFT 2021
Publisher	Scitepress
Volume	1
ISSN (Electronic)	2184-2833

Conference

Conference	International Conference on Software Technologies 2021
Abbreviated title	ICSOFT 2021
Period	6/07/21 → 8/07/21
Internet address	https://www.scitepress.org/ProceedingsDetails.aspx?ID=VnrgMHENH6Q=&t=1 (Proceedings)

Keywords

App Reviews
End-user
Human-centred Design
Human-centric Issues
Machine Learning

Access to Document

10.5220/0010576503860397

Cite this

Mathews, C., Ye, K., Grozdanovski, J., Marinelli, M., Zhong, K., Khalajzadeh, H., Obie, H., & Grundy, J. (2021). AH-CID: a tool to automatically detect human-centric issues in app reviews. In H.-G. Fill, M. van Sinderen, & L. Maciaszek (Eds.), Proceedings of the 16th International Conference on Software Technologies (pp. 386-397). (Proceedings of the 16th International Conference on Software Technologies, ICSOFT 2021; Vol. 1). Scitepress. https://doi.org/10.5220/0010576503860397

Mathews, Collins ; Ye, Kenny ; Grozdanovski, Jake et al. / AH-CID : a tool to automatically detect human-centric issues in app reviews. Proceedings of the 16th International Conference on Software Technologies. editor / Hans-Georg Fill ; Marten van Sinderen ; Leszek Maciaszek. Scitepress, 2021. pp. 386-397 (Proceedings of the 16th International Conference on Software Technologies, ICSOFT 2021).

@inproceedings{868b656cbb99411491a83da2c45ebafa,

title = "AH-CID: a tool to automatically detect human-centric issues in app reviews",

abstract = "In modern software development, there is a growing emphasis on creating and designing around the end-user. This has sparked the widespread adoption of human-centred design and agile development. These concepts intersect during the user feedback stage in agile development, where user requirements are re-evaluated and utilised towards the next iteration of development. An issue arises when the amount of user feedback far exceeds the team{\textquoteright}s capacity to extract meaningful data. As a result, many critical concerns and issues may fall through the cracks and remain unnoticed, or the team must spend a great deal of time in analysing the data that can be better spent elsewhere. In this paper, a tool is presented that analyses a large number of user reviews from 24 mobile apps. These are used to train a machine learning (ML) model to automatically generate the probability of the existence of human-centric issues, to automate and streamline the user feedback review analysis process. Evaluation shows an improved ability to find human-centric issues of the users.",

keywords = "App Reviews, End-user, Human-centred Design, Human-centric Issues, Machine Learning",

author = "Collins Mathews and Kenny Ye and Jake Grozdanovski and Marcus Marinelli and Kai Zhong and Hourieh Khalajzadeh and Humphrey Obie and John Grundy",

note = "Funding Information: Support for this work from ARC Laureate Program FL190100035 and ARC Discovery DP200100020 is gratefully acknowledged. Publisher Copyright: Copyright {\textcopyright} 2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved Copyright: Copyright 2021 Elsevier B.V., All rights reserved.; International Conference on Software Technologies 2021, ICSOFT 2021 ; Conference date: 06-07-2021 Through 08-07-2021",

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doi = "10.5220/0010576503860397",

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series = "Proceedings of the 16th International Conference on Software Technologies, ICSOFT 2021",

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editor = "Hans-Georg Fill and {van Sinderen}, Marten and Leszek Maciaszek",

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Mathews, C, Ye, K, Grozdanovski, J, Marinelli, M, Zhong, K, Khalajzadeh, H, Obie, H & Grundy, J 2021, AH-CID: a tool to automatically detect human-centric issues in app reviews. in H-G Fill, M van Sinderen & L Maciaszek (eds), Proceedings of the 16th International Conference on Software Technologies. Proceedings of the 16th International Conference on Software Technologies, ICSOFT 2021, vol. 1, Scitepress, pp. 386-397, International Conference on Software Technologies 2021, 6/07/21. https://doi.org/10.5220/0010576503860397

AH-CID: a tool to automatically detect human-centric issues in app reviews. / Mathews, Collins; Ye, Kenny; Grozdanovski, Jake et al.
Proceedings of the 16th International Conference on Software Technologies. ed. / Hans-Georg Fill; Marten van Sinderen; Leszek Maciaszek. Scitepress, 2021. p. 386-397 (Proceedings of the 16th International Conference on Software Technologies, ICSOFT 2021; Vol. 1).

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

TY - GEN

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AU - Mathews, Collins

AU - Ye, Kenny

AU - Grozdanovski, Jake

AU - Marinelli, Marcus

AU - Zhong, Kai

AU - Khalajzadeh, Hourieh

AU - Obie, Humphrey

AU - Grundy, John

N1 - Conference code: 16th

PY - 2021

Y1 - 2021

N2 - In modern software development, there is a growing emphasis on creating and designing around the end-user. This has sparked the widespread adoption of human-centred design and agile development. These concepts intersect during the user feedback stage in agile development, where user requirements are re-evaluated and utilised towards the next iteration of development. An issue arises when the amount of user feedback far exceeds the team’s capacity to extract meaningful data. As a result, many critical concerns and issues may fall through the cracks and remain unnoticed, or the team must spend a great deal of time in analysing the data that can be better spent elsewhere. In this paper, a tool is presented that analyses a large number of user reviews from 24 mobile apps. These are used to train a machine learning (ML) model to automatically generate the probability of the existence of human-centric issues, to automate and streamline the user feedback review analysis process. Evaluation shows an improved ability to find human-centric issues of the users.

AB - In modern software development, there is a growing emphasis on creating and designing around the end-user. This has sparked the widespread adoption of human-centred design and agile development. These concepts intersect during the user feedback stage in agile development, where user requirements are re-evaluated and utilised towards the next iteration of development. An issue arises when the amount of user feedback far exceeds the team’s capacity to extract meaningful data. As a result, many critical concerns and issues may fall through the cracks and remain unnoticed, or the team must spend a great deal of time in analysing the data that can be better spent elsewhere. In this paper, a tool is presented that analyses a large number of user reviews from 24 mobile apps. These are used to train a machine learning (ML) model to automatically generate the probability of the existence of human-centric issues, to automate and streamline the user feedback review analysis process. Evaluation shows an improved ability to find human-centric issues of the users.

KW - App Reviews

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PB - Scitepress

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ER -

Mathews C, Ye K, Grozdanovski J, Marinelli M, Zhong K, Khalajzadeh H et al. AH-CID: a tool to automatically detect human-centric issues in app reviews. In Fill HG, van Sinderen M, Maciaszek L, editors, Proceedings of the 16th International Conference on Software Technologies. Scitepress. 2021. p. 386-397. (Proceedings of the 16th International Conference on Software Technologies, ICSOFT 2021). doi: 10.5220/0010576503860397

AH-CID: a tool to automatically detect human-centric issues in app reviews

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

ValDefFixApp: Values-oriented Defect Fixing for Mobile Software Applications

HCMDSE: Human-centric Model-driven Software Engineering

Cite this

AH-CID: a tool to automatically detect human-centric issues in app reviews

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Projects

ValDefFixApp: Values-oriented Defect Fixing for Mobile Software Applications

HCMDSE: Human-centric Model-driven Software Engineering

Cite this