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To compress code context safely, select it for the specific task while preserving the repository’s dependency map: the relevant files, symbols, interfaces, and tests. Prune low-relevance implementation detail only when the task and validation support it. A shorter prompt is not proof that the model still has the dependencies it needs.
Why dependencies can disappear when code context is compressed
Generic text-pruning methods can miss the structure that makes code useful: functions, calls, types, imports, and relationships across files. LongCodeZip addresses this by ranking functions for relevance to an instruction, then selecting blocks within a token budget. Its conference description says earlier pruning methods often overlooked code-specific structure and dependencies. LongCodeZip (ACL 2025)
The risk is not only that a model will fail to compile. It may also recreate a helper that already exists, call an API incorrectly, or produce a plausible change that does not fit the repository. RepoExec evaluates execution, functional correctness, and dependency use; its authors report that full dependency context performed best in their experiments and that smaller contexts could mislead. RepoExec (Findings of NAACL 2025)
Use a dependency-aware compression workflow
1. Define the task before selecting files
Decide whether the model needs to complete a function, fix a bug, explain code, or make a cross-file change. The right context depends on that task. LongCodeZip uses instruction-conditioned relevance ranking, while LongLLMLingua describes query-aware selection and reorganization for long prompts more generally. LongCodeZip and LongLLMLingua (Microsoft Research)
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2. Map the relationships the change depends on
Before pruning, trace the target code to its imports, callers, callees, types, interfaces, configuration, and relevant tests. Preserve the relationships between files even if you do not include every implementation body. Hierarchical Context Pruning (HCP) models repositories at function level and retains topological dependencies between files while removing irrelevant code. In its repository-completion experiments, the authors found that removing dependent-file function implementations did not significantly reduce completion accuracy, while retaining dependency topology mattered. That finding applies to the studied setting; it is not a guarantee for every task. Hierarchical Context Pruning (2024 preprint)
3. Keep the target and relevant interfaces; trim selectively
Retain the code being changed and the dependency information needed to use it correctly. Then reduce low-relevance implementation detail at function or block level. If a dependency’s full body is unnecessary, keep enough information to make its role clear—such as its file path, symbol name, signature, or a concise note describing what it provides. This is a practical way to make omitted context recoverable, rather than a universal requirement established by the cited studies.
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HCP reports reducing input from more than 50,000 tokens to approximately 8,000 in its repository-completion experiments. LongCodeZip reports up to 5.6× compression without degrading performance across its evaluated code-completion, summarization, and question-answering tasks. Those are results from different studies and tasks, not safe compression targets for every repository or prompt. HCP and LongCodeZip
4. Validate the compressed context against the task
Where practical, compare the proposed context with a fuller version on the same task. Check whether the result executes and passes targeted functional tests, and whether it uses the project’s existing dependencies rather than unnecessarily replacing them. RepoExec evaluates executability, functional correctness, and dependency utilization. Its Dependency Invocation Rate (DIR) measures use of available dependencies; the authors report an improvement of over 10% in DIR for their instruction-tuning dataset under their experimental setup. The paper evaluated 18 models. These findings support checking dependency use as well as output quality, but do not predict results for a particular repository. RepoExec
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5. Restore missing context when a check identifies a gap
If a test or review reveals a missing symbol, contract, or cross-file relationship, add the relevant source or interface back into the context and rerun the task. Prefer restoring the missing relationship over increasing the whole prompt indiscriminately. This is an operational recommendation based on the studies’ focus on dependency preservation and evaluation, not a universal procedure tested by them.
What the reported compression results do—and do not—show
The studies use different models, datasets, tasks, and evaluation methods, so their figures are not directly comparable. HCP studies repository-level code completion with six repository-pretrained code models; RepoExec studies repository-level code generation with 18 models; LongCodeZip reports on several code tasks. None establishes one universally safe compression ratio.
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- LongCodeZip: up to 5.6× compression without degraded task performance across its evaluated code tasks. ACL 2025 conference page
- HCP: more than 50,000 tokens reduced to approximately 8,000 in its repository-completion experiments. 2024 preprint
- RepoExec: full dependency context performed best in the authors’ experiments, while smaller contexts could mislead; over 10% DIR improvement was reported for the paper’s instruction-tuning dataset and setup. Findings of NAACL 2025
For high-risk changes across several files, or whenever the dependency map is uncertain, keep fuller context until task-level checks show what can safely be removed.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use general prompt-compression results cautiously
LongLLMLingua provides background on query-aware prompt selection and position bias, but its reported benchmark numbers are not evidence that source-code dependencies survive compression. Microsoft Research’s Memento article describes a different kind of state-compression pipeline: its judge-rubric pass rate rose from 28% after single-pass compression to 92% after two rounds of judge feedback. The article also describes OpenMementos as containing 228K annotated traces, about 6× trace-level compression, and 19% code traces. Those are mixed-trace results, not repository-level code benchmarks. LongLLMLingua and Memento (Microsoft Research, 2026)
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