Towards the Next Generation of Software: Insights from Grey Literature on AI-Native Applications
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911157902639104 |
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| author | Cao, Lingli Li, Shanshan Fan, Ying Li, Danyang Zhong, Chenxing |
| author_facet | Cao, Lingli Li, Shanshan Fan, Ying Li, Danyang Zhong, Chenxing |
| contents | Background: The rapid advancement of large language models (LLMs) has given rise to AI-native applications, a new paradigm in software engineering that fundamentally redefines how software is designed, developed, and evolved. Despite their growing prominence, AI-native applications still lack a unified engineering definition and architectural blueprint, leaving practitioners without systematic guidance for system design, quality assurance, and technology selection.
Objective: This study seeks to establish a comprehensive understanding of AI-native applications by identifying their defining characteristics, key quality attributes, and typical technology stacks, as well as by clarifying the opportunities and challenges they present.
Method: We conducted a grey literature review, integrating conceptual perspectives retrieved from targeted Google and Bing searches with practical insights derived from leading open-source projects on GitHub. A structured protocol encompassing source selection, quality assessment, and thematic analysis was applied to synthesize findings across heterogeneous sources.
Results: We finally identified 106 studies based on the selection criteria. The analysis reveals that AI-native applications are distinguished by two core pillars: the central role of AI as the system's intelligence paradigm and their inherently probabilistic, non-deterministic nature. Critical quality attributes include reliability, usability, performance efficiency, and AI-specific observability. In addition, a typical technology stack has begun to emerge, comprising LLM orchestration frameworks, vector databases, and AI-native observability platforms. These systems emphasize response quality, cost-effectiveness, and outcome predictability, setting them apart from conventional software systems.
Conclusion: This study is the first to propose a dual-layered engineering blueprint... |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_13144 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards the Next Generation of Software: Insights from Grey Literature on AI-Native Applications Cao, Lingli Li, Shanshan Fan, Ying Li, Danyang Zhong, Chenxing Software Engineering Background: The rapid advancement of large language models (LLMs) has given rise to AI-native applications, a new paradigm in software engineering that fundamentally redefines how software is designed, developed, and evolved. Despite their growing prominence, AI-native applications still lack a unified engineering definition and architectural blueprint, leaving practitioners without systematic guidance for system design, quality assurance, and technology selection. Objective: This study seeks to establish a comprehensive understanding of AI-native applications by identifying their defining characteristics, key quality attributes, and typical technology stacks, as well as by clarifying the opportunities and challenges they present. Method: We conducted a grey literature review, integrating conceptual perspectives retrieved from targeted Google and Bing searches with practical insights derived from leading open-source projects on GitHub. A structured protocol encompassing source selection, quality assessment, and thematic analysis was applied to synthesize findings across heterogeneous sources. Results: We finally identified 106 studies based on the selection criteria. The analysis reveals that AI-native applications are distinguished by two core pillars: the central role of AI as the system's intelligence paradigm and their inherently probabilistic, non-deterministic nature. Critical quality attributes include reliability, usability, performance efficiency, and AI-specific observability. In addition, a typical technology stack has begun to emerge, comprising LLM orchestration frameworks, vector databases, and AI-native observability platforms. These systems emphasize response quality, cost-effectiveness, and outcome predictability, setting them apart from conventional software systems. Conclusion: This study is the first to propose a dual-layered engineering blueprint... |
| title | Towards the Next Generation of Software: Insights from Grey Literature on AI-Native Applications |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2509.13144 |