Obie, H.O., Du, H., Madampe, K. et al. Automated detection, categorisation and developers’ experience with the violations of honesty in mobile apps. Empir Software Eng 28, 134 (2023). https://doi.org/10.1007/s10664-023-10361-4
Automated detection, categorisation and developers’ experience with the violations of honesty in mobile apps
|Author:||Obie, Humphrey O.1; Du, Hung2; Madampe, Kashumi1;|
1Monash University, Melbourne, Australia
2Deakin University, Melbourne, Australia
3RMIT University, Melbourne, Australia
4Data Science Nigeria, Lagos, Nigeria
5School of Software, Beihang University, Beijing, China
6CSIRO’s Data61, Melbourne, Australia
7University of Oulu, Oulu, Finland
|Online Access:||PDF Full Text (PDF, 8.8 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe20231002138013
|Publish Date:|| 2023-10-02
Human values such as honesty, social responsibility, fairness, privacy, and the like are things considered important by individuals and society. Software systems, including mobile software applications (apps), may ignore or violate such values, leading to negative effects in various ways for individuals and society. While some works have investigated different aspects of human values in software engineering, this mixed-methods study focuses on honesty as a critical human value. In particular, we studied (i) how to detect honesty violations in mobile apps, (ii) the types of honesty violations in mobile apps, and (iii) the perspectives of app developers on these detected honesty violations. We first develop and evaluate 7 machine learning (ML) models to automatically detect violations of the value of honesty in app reviews from an end-user perspective. The most promising was a Deep Neural Network model with F1 score of 0.921. We then conducted a manual analysis of 401 reviews containing honesty violations and characterised honesty violations in mobile apps into 10 categories: unfair cancellation and refund policies; false advertisements; delusive subscriptions; cheating systems; inaccurate information; unfair fees; no service; deletion of reviews; impersonation; and fraudulent-looking apps. A developer survey and interview study with mobile developers then identified 7 key causes behind honesty violations in mobile apps and 8 strategies to avoid or fix such violations. The findings of our developer study also articulate the negative consequences that honesty violations might bring for businesses, developers, and users. Finally, the app developers’ feedback shows that our prototype ML-based models can have promising benefits in practice.
Empirical software engineering
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
113 Computer and information sciences
This work is supported by ARC Discovery Grant DP200100020. Madampe and Grundy are supported by ARC Laureate Fellowship FL190100035. Open Access funding enabled and organized by CAUL and its Member Institutions.
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