LLM-Based Dependency Detection

• LLM-Based Dependency Detection is a set of techniques that use large language models to automatically identify and structure various dependency types in code, data, and tasks.
• These methods combine multi-step reasoning, structured JSON outputs, and validation techniques to improve recall and precision over classical static analysis.
• Applications include automated code repair, repository analysis, and task decomposition, though challenges like hallucination and context management persist.

LLM-based dependency detection encompasses a collection of methodologies that leverage LLMs to identify, reason over, and exploit dependency relationships in code, data, tasks, and software artifacts. These dependencies can be syntactic (e.g., control and data flow in source code), semantic (service/data relationship in system specifications), logical (multi-step task constraints), or even extend to areas such as resource management, repository infrastructure, and structured data synthesis. The contemporary literature documents rapid progress and diverse application domains, each introducing unique technical and practical challenges, and corresponding solutions.

1. Foundations and Scope of LLM-Based Dependency Detection

Dependency detection is fundamental to program analysis, software engineering, data management, and automated reasoning. At its core, the task involves extracting and representing relationships—such as "A must precede B," "variable x influences variable y," "function foo depends on bar," "resource acquired at line m must be released before n," or "feature f2 structurally constrains feature f5." Classical approaches rely on static analysis, manual heuristics, or expert-defined rules, which have well-known completeness and precision limitations, and are often brittle in the presence of new, incomplete, or heterogeneous artifacts.

Recent advances exploit the code and semantic understanding capabilities of LLMs to replace or augment these rigid techniques. LLMs are prompted with source, artifacts, or formal specifications, inferring explicit and implicit dependencies as structured outputs (e.g., labeled nodes in dependency graphs), facts (e.g., acquisition, release, and validation events), or reasoning traces. This paradigm shift underpins recent work in source analysis (Wang et al., 2023), repository-scale understanding (Du et al., 9 Mar 2025), task decomposition (Wang et al., 13 Nov 2024), program repair (Feng et al., 5 Dec 2024), tabular data modeling (Yang et al., 25 Jul 2025), code generation from specifications (Mao et al., 5 Aug 2025), and more.

2. Key Methodological Advances

a. Code-Level Resource and Dataflow Dependency Detection

Work such as InferROI (Wang et al., 2023) employs LLMs (e.g., GPT-4) to infer resource-management intentions (ACQ/REL/VAL) in code, superseding static patterns and heuristics. The LLM outputs formalized intentions directly mapped onto control-flow graphs for a two-stage path and cross-path analysis, achieving substantially higher recall and competitive precision against classic detectors. Similarly, LLMDFA (Wang et al., 16 Feb 2024) combines LLM-guided extraction of sources/sinks, few-shot chain-of-thought summarization of dataflow, and SMT-based path verification for robust dependency fact discovery—even in incomplete, uncompiled code.

b. Repository, Artifact, and Task Dependency Recognition

Repository-level dependency understanding is benchmarked in DEPENDEVAL (Du et al., 9 Mar 2025) and DI-BENCH (Zhang et al., 23 Jan 2025). These frameworks test whether LLMs can reconstruct inter-file call graphs, regenerate package dependency configurations, and ensure end-to-end repository executability. DI-BENCH shows that even advanced LLMs achieve only a 42.9% execution pass rate, with challenges in deduplication, hallucination, and metadata errors.

For task decomposition in robotics, DART-LLM (Wang et al., 13 Nov 2024) models dependency relationships between subtasks using a directed acyclic graph (DAG). LLMs are prompted to map natural language instructions into structured JSON outputs encoding task break-down, parameters, and explicit dependency arrays, which then govern downstream multi-robot execution for both sequential and parallelizable task sets.

c. Specification-Driven Data Dependency Inference

UML2Dep (Mao et al., 5 Aug 2025) demonstrates LLM-based dependency detection in the context of industrial code generation from enriched UML sequence diagrams. A formal, two-step pipeline first constructs a decision table– and API-augmented UML diagram, then extracts a data dependency graph by prompting the LLM with mathematically formalized tasks and context-pruned inputs, ensuring unambiguous data flow representation prior to code synthesis. Mathematical constraints (e.g., execution reachability, completeness/consistency) are encoded and LLMs output machine-readable dependency graphs, directly guiding high-precision code construction.

d. Sparse and Efficient Graph Discovery in Data

For tabular data augmentation, SPADA (Yang et al., 25 Jul 2025) uses LLMs to induce sparse dependency graphs, identifying minimal parent sets for each feature via LLM-prompted queries. Acyclicity is enforced with integer linear programming to avoid cycles, and downstream generation uses kernel density estimation or conditional normalizing flows. This approach achieves 4% fewer constraint violations and orders-of-magnitude speedup compared to dense, LLM-autoregressive baselines.

e. Multi-Agent and Collaborative Dependency Modeling

ColaUntangle (Hou et al., 22 Jul 2025) proposes untangling code commits by explicitly separating explicit (control/data flow) and implicit (semantic/textual) dependencies. LLM-driven agents are assigned to each class of dependency, with a reviewer agent iterating between and consolidating agent recommendations, all operating over multi-version program dependency graphs (δ-PDGs). This yields superior accuracy (improving by up to 100% over baselines), especially in multi-concern tangled commits.

3. Benchmarking and Evaluation Strategies

Robust benchmarking is integral to progress. DEPENDEVAL (Du et al., 9 Mar 2025) introduces multi-tiered tasks—dependency recognition, repository construction, and multi-file editing—and formal evaluation metrics (e.g., Exact Match Rate, F1com for node/edge matches, aggregate scoring across correctness, alignment, functionality, and quality). DI-BENCH (Zhang et al., 23 Jan 2025) combines textual (precision, recall, fake rate) and CI-based execution metrics for ground-truth verification of dependency inference in real repositories. These frameworks expose gaps in current LLM performance, with especially acute challenges in long-context, large-repository, and cross-file scenarios.

In code-level settings, performance is rated by detection/false alarm rates (InferROI: 59.3%, 18.6% on DroidLeaks), comparative bug discovery (14–45 more bugs found over SpotBugs/Infer/PMD), and ablation studies confirming the necessity of integrating LLM inference with static analysis. For code generation from specifications, metrics include compilation/unit test pass rates (UML2Dep + DDI: +8.83% and +11.66% over baseline).

4. Technical Challenges and Mitigation Strategies

a. Hallucination and Error Propagation

LLMs are prone to hallucination—incorrect inferences or fabrications—especially when dealing with long context, sparse ground-truth, or novel artifacts. Frameworks such as LLMDFA (Wang et al., 16 Feb 2024) address this by synthesizing deterministic extraction scripts and SMT-based validators, running iterative feedback loops to correct or refine outputs. SmartHalo (Liao et al., 15 Jan 2025) validates LLM-suggested code changes with symbolic execution and static rule checks; non-equivalent outputs are rejected or retried.

b. Context Management and Pruning

Excess context increases token costs and the likelihood of LLM confusion. Reachability-based context pruning (Mao et al., 5 Aug 2025) and context-expansion with minimal relevant predecessor sets enable precise, efficient reasoning about dependencies, focusing the LLM on the core variable/api/message relationships influencing a target.

c. Structural and Output Format Constraints

Output format design significantly impacts dependency detection robustness. Multi-step, chain-of-thought instruction strategies and simplified tabular output formats (e.g., CoNLL-U–like (Matsuda et al., 11 Jun 2025)) break complex inference into manageable substeps and encourage structurally valid outputs. Structured, schema-constrained outputs (JSON) are widely adopted for both code and task decomposition settings.

d. Model Adaptation and Cross-Language Generalization

Multilingual fine-tuning (LoRA, SFT) and explicit prompt engineering are exploited to improve cross-language and cross-domain applicability. For example, (Matsuda et al., 11 Jun 2025) demonstrates high accuracy in dependency parsing across 17 languages with a single, multilingual model.

5. Practical Impact, Limitations, and Future Directions

LLM-based dependency detection delivers:

• Superior coverage for non-standard, evolving, or previously unseen APIs and dependency types (Wang et al., 2023).
• Greater adaptability to heterogeneous codes, multiple programming languages, and partially specified repositories (Wang et al., 16 Feb 2024, Du et al., 9 Mar 2025).
• Automated reduction of false positives and context-induced errors through explicit validation, pruning, and agent collaboration (Wang et al., 13 Nov 2024, Hou et al., 22 Jul 2025, Yang et al., 25 Jul 2025).
• Foundational advancements for static/dynamic bug detection, automated repair, code generation, test synthesis, task scheduling, and security anomaly detection across real-world settings.

However, performance remains bounded by:

• Sensitivity to prompt design, example selection, and context size (Alrashedy et al., 2023, Du et al., 9 Mar 2025).
• LLM hallucination and brittleness in highly novel or adversarial instances (Zhang et al., 23 Jan 2025, Matsuda et al., 11 Jun 2025).
• Computational overhead of complex token/feedback cycles in extensive repositories (Du et al., 9 Mar 2025).
• Necessity for more granular, explainable reasoning—especially in higher-level or semantic dependencies untangling (Hou et al., 22 Jul 2025).

Current research avenues seek to incorporate stronger mathematical constraints, agentic iterations, cross-modal integration (such as vision–language robotics with explicit dependency modeling (Wang et al., 13 Nov 2024)), and formalized evaluation under open-ended, real-world specifications (Mao et al., 5 Aug 2025).

6. Mathematical and Formal Foundations

Many frameworks formalize dependency structures and their reasoning processes. Representative examples include:

• Resource intention notation: ACQ(var, lineno), REL(var, lineno), VAL(var, lineno) (Wang et al., 2023).
• Path analysis algorithms and control flow: CFG traversal with counters for resource management (Wang et al., 2023).
• Data dependency graphs: $$G_{DD} = (\mathcal{V}, \mathcal{E}_{DD}, \mathcal{D})$$ with typed nodes and edges, reachability relations ($\text{reachable}(s,t)$), and rigorous data production/consumption sets (Mao et al., 5 Aug 2025).
• ILP minimization for acyclic sparse dependency graphs in synthesis: $$\mathcal{O}_{\text{ILP}} = \min \sum_{(f_i \to f_j) \in \mathcal{E}} e_{f_i \to f_j}$$ subject to DAG constraints (Yang et al., 25 Jul 2025).
• Repository-level evaluation metrics: Exact Match Rate (EMR), composite F1, and multi-criteria scores (correctness, alignment, quality) (Du et al., 9 Mar 2025).

These formal elements play a central role in ensuring the completeness, efficiency, and reproducibility of LLM-based dependency detection procedures.

7. Outlook and Unresolved Issues

The emergence of LLM-based dependency detection marks a shift towards more intelligent, context-aware, and adaptable analysis tools in program analysis, software engineering, and data science. The approach combines the domain-agnostic strengths of advanced LLMs with formalized representations and verification procedures. While substantial progress is evident, ongoing work continues to address issues of efficiency, hallucination, context management, and explainability, as well as to broaden applicability to new domains and artifact types. The synthesis of symbolic reasoning, explicit dependency modeling, and agentic collaboration promises further improvements across automation tasks that require deep dependency understanding.