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Enhancing Mobile App Selection with AI: A Voice-Activated System for App Ranking using LLM and ISO 25062 Standards
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 186 - Number 76 |
| Year of Publication: 2025 |
| Authors: Majeti Srinadh Swamy, Afisu Hrushikesh, Deshpande Pradnesh Pravin, Gudipati Sai Krishna |
10.5120/ijca2025924671
|
Majeti Srinadh Swamy, Afisu Hrushikesh, Deshpande Pradnesh Pravin, Gudipati Sai Krishna . Enhancing Mobile App Selection with AI: A Voice-Activated System for App Ranking using LLM and ISO 25062 Standards. International Journal of Computer Applications. 186, 76 ( Apr 2025), 30-33. DOI=10.5120/ijca2025924671
Abstract
As there are millions of mobile applications (app) out there, choosing the top app in a specific category is still problematic for users. This article presents a voice-based system that allows users to ask about app categories and ranks using speech, riding on state-of-the-art Natural Language Processing (NLP) and Large Language Models (LLMs). The system retrieves Play Store reviews to determine app usability scores, which offer data-driven insights to users and developers. The system combines Python's speech recognition and text-to-speech (TTS) features to provide smooth voice interactions. Selenium is used to automate web scraping for gathering app reviews, which are analyzed using NLP methods like sentiment analysis, topic modeling, and feature extraction. Besides this, the system also identifies important usability issues, providing actionable recommendations to developers to enhance app quality and user experience. The results are assessed according to the ISO 25062 usability standard, with 97% accuracy in app ranking and usability evaluation. Experimental assessments prove the system's capability in detecting high-performing apps while uncovering areas of major improvement. This work contributes to Human-Computer Interaction (HCI) and app development by presenting an AI-based usability evaluation method, ultimately favoring end-users and developers.
References
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