Code Llama Variants with Extended Context Windows under Syntax Perturbations in API Generation

Abstract

Abstract: This report synthesises findings from 10 peer-reviewed papers addressing the following research question: How robust are Code Llama variants with expanded context windows to syntax perturbations in cross-library API generation, as measured by pass@1 on BigCodeBench. Statistical language modeling and translation with transformers have found many successful applications in program understanding and generation tasks, setting high benchmarks for tools in modern software development environments. The finite context window of these neural models. 0 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 5.5/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How robust are Code Llama variants with expanded context windows to syntax perturbations in cross-library API generation, as measured by pass@1 on BigCodeBench?

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