DeepSeek-R1 and Claude Performance on SWE-Bench Verified with Issue-Specific File Context

Abstract

Abstract: This report synthesises findings from 11 peer-reviewed papers addressing the following research question: How does the inclusion of issue-specific file context affect the pass@1 accuracy of DeepSeek-R1 versus Claude on SWE-bench Verified compared to baseline context-free evaluations. As Large Language Models (LLMs) become increasingly integrated into secure software development workflows, a critical question remains unanswered: can these models not only detect insecure code but also reliably classify vulnerabilities according to standardized taxonomies? In. 8 claims were extracted from source literature; 7 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.3/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does the inclusion of issue-specific file context affect the pass@1 accuracy of DeepSeek-R1 versus Claude on SWE-bench Verified compared to baseline context-free evaluations?

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