Persistent vs. Ephemeral: A Comparative Analysis of Codebase Indexing in AI Programming Tools
Mohd Tabish Khan
Scholar (B.Tech) Department of Computer Science & Engineering, Shri Ramswaroop Memorial University, Deva Road, Lucknow
Durgesh Yadav
Scholar (B.Tech) Department of Computer Science & Engineering, Shri Ramswaroop Memorial University, Deva Road, Lucknow
Kunal Kumar
Scholar (B.Tech) Department of Computer Science & Engineering, Shri Ramswaroop Memorial University, Deva Road, Lucknow
Jayant Sharma
Scholar (B.Tech) Department of Computer Science & Engineering, Shri Ramswaroop Memorial University, Deva Road, Lucknow
Farheen Siddiqui
Assistant Professor, Department of Computer Science & Engineering, Shri Ramswaroop Memorial University, Deva Road, Lucknow
Dr. Yusuf Perwej
Professor, Department of Computer Science & Engineering, Shri Ramswaroop Memorial University, Deva Road, Lucknow
📌 DOI: https://doi.org/10.63920/tjths.52011
🔑 Keywords: AI Programming Tools, Codebase Indexing, Persistent Indexing, Ephemeral Indexing, Retrieval-Augmented Generation, Large Language Models, Developer Tools, Context Management
📅 Publication Date: 05 April 2026
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Abstract:
The rapid proliferation of AI-powered programming assistants has introduced a fundamental architectural divergence: persistent indexing versus ephemeral indexing. Persistent indexing maintains pre-computed, durable code representations stored between sessions, while ephemeral indexing constructs context on-the-fly without retaining state. This paper provides a rigorous comparative analysis of both paradigms through examination of five leading tools—GitHub Copilot,[1] Cursor,[2] Codium, Aider, and Amazon Q Developer.[3] We draw on three independently verified empirical studies: Ding et al.[6] demonstrate a 33.94% relative improvement in exact match accuracy when cross-file context (enabled by persistent indexing) is provided; Peng et al.[10] report 55.8% faster task completion with AI-assisted coding; and Morris et al.[11] show that 92% of 32-token text inputs can be reconstructed from stored embeddings, establishing a privacy risk relevant to persistent index storage. Findings indicate neither paradigm universally dominates; the optimal choice is governed by codebase size, privacy requirements, team scale, and workflow characteristics.
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📖 How to Cite
Mohd Tabish K., Durgesh Y., Kunal K., Jayant S., Farheen S., Yusuf Perwej (2026). Persistent vs. Ephemeral: A Comparative Analysis of Codebase Indexing in AI Programming Tools. TEJAS J. Technol. Humanit. Sci.,, Vol. 05, Issue 02. https://doi.org/10.63920/tjths.52011
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References
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