Towards Extracting Ethical Concerns-related Software Requirements from App Reviews

TL;DR

This paper introduces a novel knowledge graph-based framework to extract ethical concerns and related software requirements from app reviews, aiming to improve ethical considerations in mobile app development.

Contribution

It presents a new approach using a knowledge graph and ontology to identify and understand ethical concerns from user reviews, enhancing requirement extraction methods.

Findings

Knowledge graph effectively captures ethical concerns

Framework reveals underlying reasons behind concerns

Preliminary results show promising extraction capabilities

Abstract

As mobile applications become increasingly integral to our daily lives, concerns about ethics have grown drastically. Users share their experiences, report bugs, and request new features in application reviews, often highlighting safety, privacy, and accountability concerns. Approaches using machine learning techniques have been used in the past to identify these ethical concerns. However, understanding the underlying reasons behind them and extracting requirements that could address these concerns is crucial for safer software solution development. Thus, we propose a novel approach that leverages a knowledge graph (KG) model to extract software requirements from app reviews, capturing contextual data related to ethical concerns. Our framework consists of three main components: developing an ontology with relevant entities and relations, extracting key entities from app reviews, and creating connections between them. This study analyzes app reviews of the Uber mobile application (a popular taxi/ride app) and presents the preliminary results from the proposed solution. Initial results show that KG can effectively capture contextual data related to software ethical concerns, the underlying reasons behind these concerns, and the corresponding potential requirements.