Mobile application accessibility is crucial for digital inclusion. This paper presents a systematic literature review (SLR) following PRISMA guidelines, synthesizing advancements in accessibility issue detection and repair techniques. We analyze representative mobile accessibility tools and industrial practices to complement SLR evidence. Nine major databases (ACM DL, IEEE Xplore, ScienceDirect, SpringerLink, Wiley, Web of Science, Scopus, CNKI, arXiv) were searched, with 76 high-quality studies published up to July 2025 analyzed. Common accessibility issues are categorized into four types—perceptibility, operability, understandability, and robustness—aligning with user capability modeling. In accessibility issue detection, evolution from static analysis to deep learning and large language models (LLMs) has shifted research from rule-based matching to semantic reasoning, enhancing automation and generalization. However, detection and repair remain loosely coupled and fragmented, with high false-positive rates and weak inter-module feedback loops. For repair, we propose a taxonomy of rule-driven, learning-based, and LLM-assisted approaches, revealing a gap between detection breadth and repair research depth. LLMs show potential for semantic understanding, issue reasoning, and repair generation, paving the way toward intelligent agents for accessibility. Looking ahead, future research should focus on semantic-enhanced detection, multimodal repair, refined user capability modeling, and unified evaluation standards—collectively advancing accessibility engineering toward an accessibility-by-default design paradigm.
Liu et al. (Tue,) studied this question.
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