Keyword-based retrieval is limited for source-code repositories when queries are expressed in natural language or concern implementation intent and code quality rather than exact tokens. This study evaluates a prototype retrieval system that combines function-level fragmentation, text-and-code embeddings, ChromaDB vector storage, LLM-derived quality metadata, and four retrieval modes: semantic, quality-filtered, hybrid, and automatic routing. The concrete evaluation uses an educational C-code corpus. The full corpus contains 563 anonymized programmer identifiers and 8,951 C files; a reproducible 10% indexed sample contains 56 programmer identifiers, 847 files, and 3,839 fragments. Across 15 manually judged queries, semantic retrieval achieved nDCG@5 of 0.820, Success@5 of 0.800, and MRR of 0.644. The automatic router selected the expected mode for all 15 queries. In a small manual audit, LLM-derived quality scores were within one point of the manual assessment for 9 of 12 fragments. Within the reported query set, semantic retrieval was the strongest overall mode, while explicit quality metadata was most useful for explicitly quality-oriented queries.
In programming courses, instructors may need to interpret whether a submission is consistent with a student's prior programming profile, especially when code similarity alone is inconclusive. Existing source-code authorship methods are often evaluated on programming-contest or open-source datasets, where reusable templates and local code patterns can produce strong author-related signal. Educational repositories present a different setting. Students solve shared assignments while their programming practices are still developing. This study uses task-aware evaluation to contrast these production contexts and tests whether repository-visible process features add information beyond final code in six matched educational comparisons. Contest data provide a high-signal contrast, with a Kick Start mean top-1 of 0.938. Educational datasets produce substantially lower attribution performance. Adding process features raises the educational mean from 0.094 to 0.233 and mean pairwise verification ROC-AUC from 0.556 to 0.752. The comparisons show that measured signal depends on production context and that process patterns can complement weak final-code signal in educational repositories. Such models are therefore appropriate only as instructor-mediated decision support, not as independent proof of authorship.