Enhancing User Trust and Interpretability in AI-Driven Feature Request Detection for Mobile App Reviews: An Explainable Approach

Mobile app developers struggle to prioritize updates by identifying feature requests within user reviews. While machine learning models can assist, their complexity often hinders transparency and trust. This paper presents an explainable Artificial Intelligence (AI) approach that combines advanced e...

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Bibliographic Details
Published in:IEEE access Vol. 12; pp. 114023 - 114045
Main Authors: Gambo, Ishaya, Massenon, Rhodes, Lin, Chia-Chen, Ogundokun, Roseline Oluwaseun, Agarwal, Saurabh, Pak, Wooguil
Format: Journal Article
Language:English
Published: Piscataway IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Analytical models
Annotations
Applications programs
Artificial intelligence
Artificial neural networks
Explainable AI
Explainable artificial intelligence
Feature extraction
feature request detection
Interactive systems
Machine learning
machine learning interpretability
mobile app development
Mobile applications
Mobile computing
Reviews
Sentiment analysis
Software engineering
software requirements
Support vector machines
Trust management
Trustworthiness
user trust
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary:Mobile app developers struggle to prioritize updates by identifying feature requests within user reviews. While machine learning models can assist, their complexity often hinders transparency and trust. This paper presents an explainable Artificial Intelligence (AI) approach that combines advanced explanation techniques with engaging visualizations to address this issue. Our system integrates a bidirectional Long Short-Term Memory (BiLSTM) model with attention mechanisms, enhanced by Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). We evaluate this approach on a diverse dataset of 150,000 app reviews, achieving an F1 score of 0.82 and 89% accuracy, significantly outperforming baseline Support Vector Machine (F1: 0.66) and Convolutional Neural Network (CNN) (F1: 0.72) models. Our empirical user studies with developers demonstrate that our explainable approach improves trust (27%) when explanations are provided and correct interpretation (73%). The system's interactive visualizations allowed developers to validate predictions, with over 80% overlap between model-highlighted phrases and human annotations for feature requests. These findings highlight the importance of integrating explainable AI into real-world software engineering workflows. The paper's results and future directions provide a promising approach for feature request detection in app reviews to create more transparent, trustworthy, and effective AI systems.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3443527