Accelerating Mobile Application Development and Testing with Artificial Intelligence: A Systematic Literature Review

Maksym Yurko

Citation: Maksym Yurko, "Accelerating Mobile Application Development and Testing with Artificial Intelligence: A Systematic Literature Review", Universal Library of Innovative Research and Studies, Volume 03, Issue 01.

Copyright: This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The article presents a systematic literature review examining how artificial intelligence methods (LLM/GenAI, computer vision, deep learning, and multi-agent architectures) accelerate mobile application development and testing within the mobile SDLC. The objective is to address a deficit of domain-specific systematisation for mobile engineering and to answer three classes of questions: which AI approaches are applied across development and QA stages, which acceleration and efficiency metrics are empirically substantiated, and which quality/security risks accompany the adoption of generative tools. The relevance is driven by the growing complexity of mobile ecosystems and the limited scalability of manual testing and script-based automation, particularly due to brittleness under GUI changes. The novelty of the review lies in synthesising evidence from 28 selected studies from 2019 to 2025, with an explicit focus on mobile-specific constraints, and in juxtaposing speed gains against a trust contour. The principal findings are as follows: AI assistants demonstrate a substantial acceleration of routine tasks (up to ~55%) and a reduction in timelines for large-scale migrations. In testing, a transition toward LLM agents is observed, enabling high coverage at both scenario and element levels, as well as resilience to UI evolution. Concurrently, a trust crisis is documented due to a significant share of vulnerabilities in generated code, dependency hallucinations, and increased technical debt, necessitating the institutionalisation of verification practices and secure-by-default principles. The article is intended to be beneficial to software engineering researchers and to mobile development/QA practitioners integrating GenAI into workflows.


Keywords: Mobile Development, Generative AI, Large Language Models, PRISMA.

Download doi https://doi.org/10.70315/uloap.ulirs.2026.0301009