Managing Large-Scale Internal Platform Evolution: AI-Assisted Tools and Techniques for Enterprise Engineering Teams

Vishnu Iyengar

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024, vol. 10, issue 6, 605-615

Abstract: As software organizations scale, the migration of internal platforms becomes increasingly critical yet exponentially more complex than traditional external platform migrations. This paper presents a comprehensive framework for managing large-scale internal platform migrations, addressing the unique challenges of transitioning business-critical systems used by thousands of engineers. We explore how organizational scale fundamentally transforms the nature of platform migration, requiring sophisticated approaches to maintain business continuity while preserving organization-specific workflows. The paper presents a comprehensive framework for approaching architectural design, development methodology, and migration execution, emphasizing the crucial role of automation and tooling infrastructure. We discuss the emerging role of AI in augmenting traditional migration approaches while maintaining the strict reliability requirements of internal platforms. By examining these various aspects of platform migration, we provide technical leaders with actionable frameworks for evolving critical internal infrastructure at scale. The paper demonstrates that successful internal platform migrations require a carefully orchestrated approach that balances standardization with organizational specificity, supported by comprehensive tooling and validation frameworks.

Keywords: Platform Migration; Internal Developer Platforms; Software Engineering at Scale; Large-scale Code Migration; Migration Automation Tools; Software Architecture Evolution; Engineering Productivity; DevOps Transformation; Technical Debt Management; AI-assisted Code Migration (search for similar items in EconPapers)
Date: 2024
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT241061101
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Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v10:y2024:i6:id:451

DOI: 10.32628/CSEIT241061101

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