Pith. sign in

REVIEW 5 cited by

CoCoMIC: Code Completion By Jointly Modeling In-file and Cross-file Context

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2212.10007 v2 pith:UAWNTKTN submitted 2022-12-20 cs.CL cs.SE

Yangruibo Ding , Zijian Wang , Wasi Uddin Ahmad , Murali Krishna Ramanathan , Ramesh Nallapati , Parminder Bhatia , Dan Roth , Bing Xiang This is my paper
classification cs.CLcs.SE
keywords codecontextcross-filecompletioncocomicin-fileincreasejointly
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

While pre-trained language models (LM) for code have achieved great success in code completion, they generate code conditioned only on the contents within the file, i.e., in-file context, but ignore the rich semantics in other files within the same project, i.e., cross-file context, a critical source of information that is especially useful in modern modular software development. Such overlooking constrains code language models' capacity in code completion, leading to unexpected behaviors such as generating hallucinated class member functions or function calls with unexpected arguments. In this work, we develop a cross-file context finder tool, CCFINDER, that effectively locates and retrieves the most relevant cross-file context. We propose CoCoMIC, a framework that incorporates cross-file context to learn the in-file and cross-file context jointly on top of pretrained code LMs. CoCoMIC successfully improves the existing code LM with a 33.94% relative increase in exact match and a 28.69% relative increase in identifier matching for code completion when the cross-file context is provided.

Discussion (0). Sign in to comment.

Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Better Call Grep: Evaluating and Improving Grep-Like Lexical Retrieval for Repository-Level Code Completion

    cs.SE 2026-01 conditional novelty 6.0 of 10

    LLM-generated ripgrep queries plus BM25 re-ranking and line-interval de-duplication outperform graph- and RL-based retrievers for repository-level code completion on CrossCodeEval and RepoEval-Updated.

  2. Enhancing Project-Specific Code Completion by Inferring Internal API Information

    cs.SE 2025-07 conditional novelty 6.0 of 10

    A retrieval-augmented code completion method that infers project-internal APIs from a rough draft and a static knowledge base, beating existing repo-level baselines.

  3. GRACE: Graph-Guided Repository-Aware Code Completion through Hierarchical Code Fusion

    cs.SE 2025-09 conditional novelty 5.0 of 10

    GRACE combines a multi-level code graph, hybrid text-structure retrieval, and graph fusion to improve repository-level code completion over vanilla and graph-based RAG baselines.

  4. SEFRQO: A Self-Evolving Fine-Tuned RAG-Based Query Optimizer

    cs.DB 2025-08 unverdicted novelty 5.0 of 10

    SEFRQO claims a self-evolving fine-tuned LLM with retrieval and execution feedback reduces query latency versus PostgreSQL, but the provided body is a different paper, blocking verification.

  5. LoCoBench: A Benchmark for Long-Context Large Language Models in Complex Software Engineering

    cs.SE 2025-09 reject novelty 4.0 of 10

    LoCoBench introduces a large synthetic long-context software engineering benchmark, but its evaluation tables contradict its own scoring formula and its headline degradation result is inverted by the data it reports.

Pith tools