Tool Call Analysis Techniques

• Tool call analysis is the systematic process of extracting and evaluating call graphs that represent tool invocations and dependencies in software systems.
• It employs static, dynamic, and hybrid methodologies to model runtime and structural properties, improving accuracy and revealing hidden call patterns.
• The analysis supports practical applications such as debugging, performance profiling, security assessments, malware detection, and LLM orchestration.

Tool call analysis is the systematic process of identifying, modeling, profiling, and reasoning about calls made to or between tools, functions, APIs, or system services within software systems. This topic encompasses both the extraction and interpretation of call graphs—directed graphs where nodes are routines or tools and edges represent invocations or dependencies—as well as the evaluation of the runtime, structural, and decision-making properties of such calls. Tool call analysis has broad applications, including program profiling, static and dynamic program analysis, workflow optimization, malware detection, security analysis, and the orchestration of LLM–driven composite systems.

1. Methodologies in Tool Call Analysis

Tool call analysis methodologies can be broadly grouped into static, dynamic, and hybrid approaches, each with distinct advantages and trade-offs:

• Static Analysis constructs the call graph through source code or binary inspection, parsing constructs such as function definitions, invocations, or imported modules. Techniques range from simple signature-based extraction and control flow graph traversal to advanced methods leveraging abstract interpretation, context-sensitive data flow, or transformer-based semantic modeling (0810.3468, Padhye et al., 2013, Le-Cong et al., 2022, Huang et al., 2023, Solanki, 28 Jan 2024, Antal et al., 12 May 2024, Thévenon et al., 23 Oct 2024, Bhuiyan et al., 22 Jun 2025).
• Dynamic Analysis observes the program during runtime, capturing real function or API invocation traces, sometimes augmented with parameter or provenance data for context (Ha et al., 2020, Muzaffar et al., 2023, Venkatesh et al., 1 Oct 2024). This approach can expose edges impossible to determine statically, particularly those hidden by dynamic dispatch, reflection, or code injection.
• Hybrid and Augmentative Approaches combine static graph extraction with dynamic validation or pruning, or augment static call graphs using machine learning to recover missing or spurious edges (Le-Cong et al., 2022, Bhuiyan et al., 22 Jun 2025).
• Specialized Approaches include binary analysis using symbolic execution for system call identification without source code access (Thévenon et al., 23 Oct 2024), and event-driven profiling which combines call graph extraction with runtime performance statistics (0810.3468, Ha et al., 2020).

Tool call analysis can also involve the identification of call “phases” in a program (distinct execution modes requiring different filtering or security policies) (Thévenon et al., 23 Oct 2024), the modeling of cross-layer or multilanguage dependencies (Shatnawi et al., 2019, Veenendaal et al., 2016), and decision-making tasks (e.g., in tool-using LLMs) around when to invoke tools rather than how (Ross et al., 26 Apr 2025).

2. Call Graph Representation and Construction

A fundamental artifact in tool call analysis is the call graph: a directed graph $G = (V, E)$ where $V$ represents code entities (functions, methods, APIs), and $E$ directed edges corresponding to call relations. The granularity of nodes and edges may reflect:

• Syntactic relationships (direct calls as seen in code or bytecode)
• Semantic dependencies (dynamic dispatch, reflection, hidden framework calls)
• Inter-layer or cross-component calls (moving between UI, business, and data layers (Veenendaal et al., 2016); or across language boundaries (Shatnawi et al., 2019))
• System calls (binary-level, e.g., tracing syscall numbers in executable code (Thévenon et al., 23 Oct 2024))

Static call graph construction can vary from simple CHA (Class Hierarchy Analysis) or field-based algorithms to flow- and context-sensitive analyses (e.g., through value-context frameworks with non-distributive data flow functions for precise call resolution in object-oriented languages (Padhye et al., 2013)), to large-scale incremental assembly on demand for package ecosystems (Keshani, 2021). Modern approaches may also employ machine-learned models: Graph Neural Networks treat the problem as link prediction over rich, multi-file AST graphs, using both syntactic and “semantic” identifier edges (Bhuiyan et al., 22 Jun 2025).

Several challenges influence call graph completeness and precision:

• Dynamically generated calls (reflection, dynamic property access, or runtime code eval)
• Multi-file and multi-language boundaries
• Framework-driven callbacks and system entry points (prevalent in modern platforms like Android (Samhi et al., 10 Jul 2024))
• Implicit lifecycle or callback registrations

The soundness and completeness of call graphs are often at odds: more precise graph construction (less over-approximation) may result in higher unsoundness due to omitted edges, as found in systematic evaluations of Android static analysis tools (Samhi et al., 10 Jul 2024).

3. Precision, Soundness, and Pruning in Tool Call Analysis

Precision and soundness are major evaluation axes:

• Soundness refers to whether the extracted call graph over-approximates all possible runtime calls. In practice, static analyses (especially for Android and dynamic languages) often miss significant portions of dynamically executed methods (e.g., up to 61% missing methods in Android app static analysis (Samhi et al., 10 Jul 2024)).
• Precision addresses the minimization of spurious (false-positive) call edges. Overly conservative static tools may include hundreds of unrealizable edges due to imprecise call target or alias information, especially when context- or flow-insensitivity is applied (Keshani, 2021, Thévenon et al., 23 Oct 2024).

To improve call graph quality:

• Pruning approaches such as AutoPruner use transformer-based models (e.g., fine-tuned CodeBERT) to capture semantic relationships between caller and callee pairs, fusing these with classical graph-structural features in a neural classifier to remove likely false-positive edges (Le-Cong et al., 2022). This improves downstream analysis accuracy (e.g., reduced false alarms in null pointer analysis).
• Augmentation with machine-learned link prediction can recover false negatives; e.g., GNNs can rank the true target in the top-5 for 72% of unresolved cases by learning from both static and dynamic edge ground truths (Bhuiyan et al., 22 Jun 2025).

Hybrid static-dynamic and ML-based augmentation point toward a broader trend: reconciling scalability, soundness, and precision by blending algorithmic and data-driven techniques.

4. Profiling, Performance, and Workflow-Level Tool Call Analysis

In addition to structural analysis, profiling approaches provide temporal and quantitative insights:

• Call-graph profilers (e.g., for GNU Octave) track not only function execution times (as in tic–toc timing or flat profile summaries) but also the full call tree, attributing self time using formulas such as

$$\text{self\_time} = \text{total\_time} - \text{tick}$$

where “tick” is time spent in callees (0810.3468). This hierarchical view enables granular performance bottleneck localization.

• Workflow-level trace tools such as Chimbuko provide real-time, distributed analysis for high-performance computing: nodes locally detect and report anomalies based on statistical thresholds (e.g., $T_\text{call} > \mu_i + \alpha \cdot \sigma_i$) and offer visualization modules for anomaly distribution, timeline navigation, and drill-down into execution provenance (Ha et al., 2020).

Profilers must balance additional tracing overhead (typically scaling with call counts or data volume) against the analytical benefits, with tolerable overhead demonstrated (e.g., <0.5% additional runtime in Octave profiling (0810.3468)).

5. Applications Across Domains

Tool call analysis supports a range of applications:

• Debugging and Program Comprehension: Automated call graph extraction—especially across multi-layered or multi-language codebases—enables rapid traversal and understanding for bug diagnosis or refactoring in large enterprise systems (Veenendaal et al., 2016, Shatnawi et al., 2019, Huang et al., 2023, Antal et al., 12 May 2024).
• Security and Sandboxing: Precise analysis of system call usage (including phase-aware filtering) reduces attack surface; binary-level tools such as B-Side can enforce application-specific policies even without source access, outperforming coarse-grained approaches (Thévenon et al., 23 Oct 2024).
• Malware and Forensic Analysis: Integrated tools that extract and correlate static API call graphs with dynamic execution traces are used for malware detection and investigation (Muzaffar et al., 2023). Temporal and network-based analyses of telephony data likewise rely on tool call analytics (Catanese et al., 2013).
• LLM Tool Orchestration: In modern LLM platforms, analysis shifts to whether and when to call a tool or issue a follow-up, with benchmarks such as When2Call evaluating scenario-appropriate decision-making (e.g., correct abstention or clarification rather than spurious tool calls) (Ross et al., 26 Apr 2025). Compiler-inspired approaches can fuse and parallelize function/tool calls, thereby reducing system latency and token usage in LLM-powered workflows (Singh et al., 7 May 2024).
• Performance Optimization: In scientific computing and call center analytics, detailed modeling of call structure, agent or resource heterogeneity, and breaks leads to more accurate simulation and staffing predictions (0810.3468, Koole et al., 29 Feb 2024).
• Ecosystem-scale Dependency Analysis: Incremental call graph assembly (as in the Maven ecosystem (Keshani, 2021)) and advanced call graph pruning improve scalability and precision for tasks like vulnerability reachability analysis and dependency risk management.

6. Current Challenges and Future Directions

Despite advances, several challenges remain prominent:

• Language and framework dynamism: Dynamic language features, reflective calls, registration-based frameworks, and multi-language source mixing complicate comprehensive call graph construction. Several studies find that static analysis yields high unsoundness unless these are explicitly modeled (e.g., 61% of methods in real Android apps are missed (Samhi et al., 10 Jul 2024)).
• Incomplete tool support for real-world code: Most call graph tools underperform on modern, multi-file projects or for new language versions (Antal et al., 12 May 2024, Venkatesh et al., 1 Oct 2024).
• Balancing scalability and precision: High-precision, context-sensitive call graph construction can incur prohibitive time and memory overhead (Padhye et al., 2013, Keshani, 2021).
• Evaluation metrics and ranking: Standard statistical or machine learning metrics are often inadequate for tool call analysis; ranking-based or domain-specific measures are increasingly adopted (Bhuiyan et al., 22 Jun 2025).
• Extending to tool orchestration and decision-making: LLM-based systems drive a focus not only on technical correctness of tool calls, but also on deciding when not to call, with optimization regimes such as preference training (Ross et al., 26 Apr 2025) and compiler-inspired function fusing (Singh et al., 7 May 2024).

Ongoing research examines hybrid methods that leverage both static and dynamic ground truths, graph-based deep models for completion or pruning, improvements in cross-language understanding, and enhanced integration with runtime, workflow-level, or LLM-focused orchestration systems.

7. Comparative Summary Table

Methodology	Key Strength	Limitation or Challenge
Static Analysis	Scalable, structured	Misses dynamic/implicit calls
Dynamic Analysis	Captures actual execution	Needs test coverage
Hybrid/ML-Augmented	Improved recall and precision	Training/label overhead
Profiling/Tracing	Performance insights, anomaly	Runtime overhead
Phase-based Analysis	Tailored filtering/policies	Requires precise phase detection

The practical value of tool call analysis continues to increase as systems grow in complexity, and its methodologies are being rapidly enriched by advances across static analysis theory, software engineering practice, and machine learning. The field remains highly active, with cross-disciplinary contributions needed to meet emerging scalability, security, and correctness requirements.