Scope Inference Agent Overview

• Scope Inference Agent is a tool that infers a semantically precise scope from a developer-initiated rename, automating coordinated refactoring.
• It employs LLM-driven reasoning, human feedback, and IDE APIs to iteratively refine renaming patterns and guard conditions across multiple files.
• Benchmark evaluations demonstrate significant improvements in precision (0.51), recall (0.58), and overall F1 (0.55) compared to traditional methods.

A Scope Inference Agent is an autonomous software module designed to infer, from a developer-initiated identifier rename, the complete scope of related program elements that should also undergo coordinated renaming. Its primary function is to extract a semantically precise, natural-language Declared Scope from the initial rename, thus enabling downstream agents to automate safe, consistent refactoring across multiple files and contexts while keeping developers involved in the decision loop. The Scope Inference Agent is central to recent multi-agent refactoring frameworks, such as the CoRenameAgent system, which leverages LLM-driven reasoning in tandem with IDE APIs and developer feedback to realize robust, human-in-the-loop coordinated renaming (Bellur et al., 1 Jan 2026).

1. Coordinated Renaming and the Need for Scope Inference

Coordinated renaming is the simultaneous refactoring of multiple, semantically related identifiers as part of a single conceptual realignment (e.g., API redesign, semantic shift). Empirical studies reveal that 57 % of renaming commits involve multiple, related identifiers, distributed over several files and code regions (Osumi et al., 2022). The principal challenge is scoping: inferring all and only those program elements for which the rename is desirable, avoiding both low recall (missed dependencies) and low precision (irrelevant suggestions). Manual propagation is tedious and error prone, while automated approaches based on syntactic analysis or heuristic expansion frequently produce false positives or incomplete recommendations (Doi et al., 2024).

2. Formal Definitions and Core Tasks

Let $r_\text{seed}$ denote the developer-initiated seed rename, represented as $$r = \langle\text{file\_path}, \text{old\_name}, \text{new\_name}, \text{line\_number}, \text{identifier\_type}\rangle$$ (Bellur et al., 1 Jan 2026). The Scope Inference Agent receives $(P, r_\text{seed}, M)$—where $P$ is the program, $r_\text{seed}$ the initiating edit, and $M$ an episodic memory of attempted renames, feedback, and prior scope declarations—and must synthesize a Declared Scope $D = \langle p, G\rangle$ (Bellur et al., 1 Jan 2026):

• $p$ is a renaming pattern (e.g., “joinHints → queryHints”).
• $G$ is a guard set (contextual preconditions/exclusions, e.g., “only private fields, not public methods”).

Subsequently, the agent must support iterative refinement, receiving feedback $h(r)=0$ (developer rejection) to exclude inappropriate contexts via synthesis of new guards $G'$.

3. Multi-Agent Architecture and Scope Inference Workflow

In CoRenameAgent (Bellur et al., 1 Jan 2026), the Scope Inference Agent operates within a three-agent architecture:

Agent	Primary Function	Input/Output
Scope Inference Agent	Extract Declared Scope (natural language, guards)	$$(P, r_\text{seed}, M) \rightarrow D = \langle p, G\rangle$$
Planned Execution Agent	File-level candidate identification and execution	$(f, D, M)$; developer review, adjustment
Replication Agent	Project-wide propagation via slicing and keyword search	$(P, D, C)$; file set $F_{\text{next}}$

The workflow proceeds as follows:

1. Developer executes a seed rename ($r_\text{seed}$) in the IDE.
2. Scope Inference Agent infers the initial scope $D$ via LLM prompting (few-shot, broad context).
3. Planned Execution Agent uses $D$ to generate, filter, and validate candidate renames within the seed file, presenting accepted candidates to the developer for review.
4. Developer rejections are recorded in $M$. The Scope Inference Agent refines $D$ by synthesizing exclusion guards to eliminate contexts leading to false positives.
5. Replication Agent extends $D$ project-wide via static slicing and semantic search, filtering files for further inspection.
6. The loop repeats until no new candidates are discovered or an iteration cap is reached.

4. Declared Scope Extraction and Guarded Refinement

Declared Scope extraction leverages an LLM (few-shot) to generate broad, natural-language descriptions of the intended rename pattern and affected contexts, initially with minimal exclusion. Guarded refinement is activated when developer rejections are detected in the Planned Execution Agent’s output. The agent synthesizes a set of guard conditions $G$ or $G'$ mapping specific negative examples (rejected suggestions) to exclusion rules, thereby tuning the scope for higher precision. Such iterative refinement is crucial to avoid “snowballing” false positives, particularly as renaming traverses across files and diverse code regions.

5. Integration with Downstream Agents and Safe Refactoring

The Scope Inference Agent’s output $D$ is strictly consumed as a plan by the Planned Execution Agent and Replication Agent. The PEA uses $D$ to (a) drive candidate identification, (b) ensure that final selections are grounded in both $p$ and $G$, and (c) invoke trusted IDE APIs for execution (e.g., IntelliJ’s renameVariable, renameMethod calls), guaranteeing referential correctness, avoidance of shadowing, and type-checking (Bellur et al., 1 Jan 2026). The episodic memory $M$ maintains the evolving $D$ and developer h/reviews for continual scope improvement.

6. Evaluation and Comparative Performance

Quantitative benchmark results across RenasBench and Co-renameBench datasets indicate that use of a Scope Inference Agent, especially with developer-in-the-loop scope refinement, achieves substantial improvements in F1-measure, recall, and precision over both heuristic-based (RENAS, RenameExpander) and vanilla LLM baselines:

System	Precision	Recall	F1
RENAS	0.14	0.73	0.24
VanillaLLM	0.31	0.17	0.22
CoRenameAgent	0.51	0.58	0.55

Ablation analysis reveals that removing scope refinement or human feedback results in catastrophic drops in precision and low overall F1. The agent-based approach further reduces average human workload and time-cost by a factor of 5–10x compared to manual propagation or earlier tools. Real-world deployments led to successful, accepted PRs in major open-source projects (Bellur et al., 1 Jan 2026).

7. Limitations, Extensions, and Prospects

The Scope Inference Agent as implemented in CoRenameAgent is language-agnostic but currently applied to Java, with reliance on IDE refactoring APIs for semantic correctness. Challenges include accuracy of LLM-generated scopes under atypical rename patterns, necessity for iterative refinement to maintain high precision as the scope broadens, and potential difficulties in associating scope declarations with disparate commits or nonlocal dependencies.

Future work targets automated synthesis of guard conditions, scope inference in non-rename refactorings (move-method, extract-class), and integration of program analyses or code search to supplement LLM-based scope identification. The agent pattern is extensible to other transformation pipelines, provided that a semantically rich, editable Declared Scope can be extracted and propagated. Human-in-the-loop review remains essential for robust, safe, coordinated refactoring at scale (Bellur et al., 1 Jan 2026).