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Towards Extracting Ethical Concerns-related Software Requirements from App Reviews

Aakash Sorathiya, Gouri Ginde

TL;DR

This work addresses the challenge of extracting ethically oriented software requirements from app reviews by proposing a knowledge-graph–driven framework. It combines ontology-based entity extraction with manual NER and explicit entity linking to build a KG that maps app reviews to underlying issues, ethical concerns, and concrete requirements, enabling visualization of interdependencies. Preliminary results on Uber reviews yield a KG with hundreds of nodes and relations and identify 14 related requirements, demonstrating the approach's potential to capture contextual reasoning behind ethical concerns. The study lays groundwork for more scalable, automated, and cross-app analyses, aiming to help developers design ethically safer software and provide users with transparent app assessments.

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.

Towards Extracting Ethical Concerns-related Software Requirements from App Reviews

TL;DR

This work addresses the challenge of extracting ethically oriented software requirements from app reviews by proposing a knowledge-graph–driven framework. It combines ontology-based entity extraction with manual NER and explicit entity linking to build a KG that maps app reviews to underlying issues, ethical concerns, and concrete requirements, enabling visualization of interdependencies. Preliminary results on Uber reviews yield a KG with hundreds of nodes and relations and identify 14 related requirements, demonstrating the approach's potential to capture contextual reasoning behind ethical concerns. The study lays groundwork for more scalable, automated, and cross-app analyses, aiming to help developers design ethically safer software and provide users with transparent app assessments.

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.
Paper Structure (5 sections, 2 figures)

This paper contains 5 sections, 2 figures.

Table of Contents

  1. Introduction
  2. Our approach
  3. Preliminary findings
  4. Related Work
  5. Conclusion and Future Plan

Figures (2)

  • Figure 1: Our study design. Entity extraction is performed based on the ontology, followed by entity linking for KG construction. KG is stored in the graph database for incremental expansion of the dataset
  • Figure 4: An example of a KG generated in Neo4j from the extracted data. The graph shows the visualization of the scenarios as described in Section \ref{['results']}. Red arrows represent the underlying reasons for the ethical concerns, and green arrows show the potential requirements suggested by users that can address the ethical concerns.