Repository Context Integration in Multimodal GNN-Based Code Completion on BIGCode

Abstract

Abstract: This report synthesises findings from 14 peer-reviewed papers addressing the following research question: What is the impact of integrating repository context (e.g., imports, parent classes) on the accuracy of code completion tasks when using multimodal GNN-based models evaluated on the BIGCode benchmark. Retrieval-augmented generation (RAG) has recently demonstrated considerable potential for repository-level code completion, as it integrates cross-file knowledge with in-file preceding code to provide comprehensive contexts for generation. To better understand the contribution. 18 claims were extracted from source literature; 2 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 4.7/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

What is the impact of integrating repository context (e.g., imports, parent classes) on the accuracy of code completion tasks when using multimodal GNN-based models evaluated on the BIGCode benchmark?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Quality Tier

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance