A cognitive agent for mining bugs reports, feature suggestions and sentiment in a mobile application store

Muñoz López, Sergio, Araque Iborra, Óscar ORCID: https://orcid.org/0000-0003-3224-0001, Fernández Llamas, Antonio and Iglesias Fernández, Carlos Ángel ORCID: https://orcid.org/0000-0002-1755-2712 (2018). A cognitive agent for mining bugs reports, feature suggestions and sentiment in a mobile application store. In: "4th International Conference on Big Data Innovations and Applications (Innovate-Data)", 06/08/2018 - 08/08/2018, Barcelona, Spain. pp. 1-8. https://doi.org/10.1109/Innovate-Data.2018.00010.

Description

Title: A cognitive agent for mining bugs reports, feature suggestions and sentiment in a mobile application store
Author/s:
Item Type: Presentation at Congress or Conference (Article)
Event Title: 4th International Conference on Big Data Innovations and Applications (Innovate-Data)
Event Dates: 06/08/2018 - 08/08/2018
Event Location: Barcelona, Spain
Title of Book: 4th International Conference on Big Data Innovations and Applications (Innovate-Data)
Date: 2018
Subjects:
Freetext Keywords: Cognitive agent; review; app; bugs; suggestions; sentiment analysis
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Otro
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Over the last years, mobile applications and their corresponding distribution platforms have gained momentum. Applications stores allow users to write reviews and ratings about the apps, giving feedback to developers. User ratings and reviews may help to improve software quality, solve bugs and develop new features. However, this data is hard to be handled by an individual due to the ever growing amount of textual reviews. This paper proposes the use of cognitive computing technologies for addressing this challenge, by developing a smart agent able to mine bugs reports, feature suggestions and sentiment expressed in mobile app reviews. The main contributions of this paper are: the design of a cognitive agent for assisting developers in managing their interaction with their users, the application of machine learning algorithms for bug and feature request detection, and the agent implementation in a real scenario.

Funding Projects

Type
Code
Acronym
Leader
Title
Government of Spain
TEC2015-68284-R
SEMOLA
Unspecified
Unspecified
Government of Spain
RTC-2016-5053-7
EMOSPACES
Unspecified
Unspecified

More information

Item ID: 54815
DC Identifier: https://oa.upm.es/54815/
OAI Identifier: oai:oa.upm.es:54815
DOI: 10.1109/Innovate-Data.2018.00010
Official URL: https://ieeexplore.ieee.org/document/8500065
Deposited by: Memoria Investigacion
Deposited on: 07 May 2019 15:00
Last Modified: 07 May 2019 15:00
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