Semantic Feature Extraction via Static Analysis

• Semantic Feature Extraction via Static Analysis is a process that identifies high-level behavioral properties in software by analyzing code structures, data flows, and control flows without execution.
• It employs methods such as bytecode parsing, control-flow graph analysis, attribute grammar evaluation, and LLM-based reasoning to extract meaningful semantic signals.
• Applications include malware detection, automated bug triage, feature-aware code search, and reverse engineering, while addressing challenges like obfuscation and noise in extraction.

Semantic Feature Extraction via Static Analysis refers to the systematic, programmatic identification of high-level, behaviorally meaningful properties of software by inspecting code or binaries without execution. The aim is to recover indicators such as intent, data flow, resource usage, or security-relevant behaviors which align closely with a program's operational semantics, rather than surface lexical or syntactic traits. Techniques span bytecode inspection, control- and data-flow modeling, attribute grammar propagation, graph construction, and—now—language-model-based semantic inference. Effective static semantic feature extraction enables downstream applications including malware detection, software comprehension, warning triage, code search, and automated category assignment.

1. Definitions and Semantics of Extracted Features

Semantic features encode evidence about what code does, not just how it is written. They are designed to abstract and distill the observed behaviors, structural motifs, permission sets, or data transformations performed by a program component. Key examples include:

• Declared Intents and Permissions: In Android, the use of specific intents (e.g., android.media.AudioRecord) and permissions (e.g., RECORD_AUDIO, SEND_SMS) that imply behavioral capabilities (Qadir et al., 2020).
• Control and Data Flows: Sequences of operations on code variables, especially as captured by paths through a control-flow graph relevant to a possible bug or warning (Zhang et al., 2023).
• API Usage Patterns and Categorization: The presence and context of sensitive API calls; their mapping to behavioral categories or threat signatures (Marais et al., 13 Jun 2025, Qadir et al., 2020).
• Type and Dataflow Relations: Symbol-table mappings, expected data types, inputs/outputs, variable initialization and use, and other attribute-grammar-derived facts (Mukherjee et al., 2021).
• Graph-Structured Relationships: Inheritance, method calls, overrides, and type references, represented as a semantic code graph (SCG) (Borowski et al., 2023).
• Binary Behavioral Abstractions: Import hashes, section anomalies, packer signatures, and MITRE ATT&CK pattern matches for PE files (Marais et al., 13 Jun 2025).
• Inter-procedural Semantics via LLMs: Natural language summaries of call sites and target functions, embedded and compared for alignment to refine indirect call targets (Cheng et al., 8 Aug 2024).

Semantic features are often formalized as elements in a binary or structured vector, as relational database entries, or as graph attributes, depending on context.

2. Methodologies and Pipelines for Static Semantic Feature Extraction

Prominent workflows for semantic feature extraction via static analysis include:

• Bytecode and Manifest Parsing: For Android, unpack APKs using APKTool, parse Dalvik-derived “smali” for intent and permission tokens, and analyze the AndroidManifest.xml for declared capabilities (Qadir et al., 2020).
• CFG and Path Extraction: Build control-flow graphs, extract program paths (from entry to a warning or identifier), and select definitions or uses on the slice of interest. Tokenize paths to capture atomic operations and control/data dependencies (Zhang et al., 2023).
• Attribute Grammar Evaluation: Compute inherited and synthesized semantic attributes on parse tree nodes—symbol tables, type environments, method signatures, variable initialization—by traversing the AST with context propagation (Mukherjee et al., 2021).
• Graph Construction and Relationship Encoding: Parse source code to emit an SCG where nodes encode code entities (classes, methods, etc.) and edges encode semantic relations (calls, extends, overrides, type references) (Borowski et al., 2023).
• Static–Expert Feature Inference for Binaries: Extract global and section metadata, import tables, packing cues, and feed into rule-based engines like CAPA, outputting expert-interpretable behavioral tags (e.g., MITRE ATT&CK) compiled into JSON (Marais et al., 13 Jun 2025).
• LLM-based Semantic Reasoning: Prompt an LLM with code snippets (or summaries) to produce semantically rich, human-readable or vectorized features—function intent, API usage, input/output signatures, or high-level behaviors (Cheng et al., 8 Aug 2024, Gagnon et al., 27 Sep 2025).
• Policy-driven Neuro-symbolic Composition: Compose parse-driven (“symbolic”) relations and LLM-inferrable (“neural”) semantic facts via a Datalog-like language; orchestrate analysis in a fixed-point computation with lazy, incremental, and parallel evaluation (Wang et al., 18 Dec 2024).

These pipelines generally proceed from byte-level or AST-level token scans through hierarchical or relational abstraction, often with integration of machine learning or neural modeling at the feature fusion or classification stage.

3. Formal Modeling and Representation

Semantic features for static analysis are encoded in a range of formal systems:

• Binary Feature Vectors: $f \in \{0,1\}^n$ where $f_j=1$ iff the feature is present; suitable for permission or intent vectors (Qadir et al., 2020).
• Graph Structures: $SCG=(V,E,s,t,\tau_V,\tau_E,\alpha_V,\alpha_E)$ where $V$ is entities, $E$ semantic relationships, and $\alpha$ provides attributes; enables centrality, modularity, and partitioning analyses (Borowski et al., 2023).
• Relational Tuples: Features as sets of $$(\text{predicate},\text{call pattern},\text{success pattern})$$ in abstract interpretation, supporting assertion-based matching (Garcia-Contreras et al., 2016).
• Attribute Embeddings: Dense vectors from concatenated or pooled attribute encodings, often with max or elementwise fusion, for input to neural models (Mukherjee et al., 2021, Guan et al., 14 Feb 2024, Zhang et al., 2023).
• Semantic Embedding Similarity: Cosine similarity of LLM-derived summary embeddings, $$Sim(S_\text{caller},S_\text{callee}) = \langle S_\text{caller}, S_\text{callee}\rangle/(\|S_\text{caller}\|_2\|S_\text{callee}\|_2)$$, controlling filtering of indirect call candidates (Cheng et al., 8 Aug 2024).

Feature selection, mapping, and similarity scoring are often codified as mappings and set operations (e.g., Jaccard similarity $$J(c) = |F_{\text{app}}\cap F_c| / |F_{\text{app}}\cup F_c|$$) to support categorization or information retrieval.

4. Applications and Impact

Static semantic feature extraction supports a broad spectrum of software engineering and security objectives:

• Malware Detection: By mapping extracted semantic features (intents, permissions, or MITRE ATT&CK labels) to known malicious patterns or detecting anomalous feature-overprivileging (Qadir et al., 2020, Marais et al., 13 Jun 2025).
• Warning Triage and Bug Detection: Distinguishing true and false-positive alarms using control-flow–path-based semantic tokenization and neural encoding (Zhang et al., 2023).
• Software Comprehension and Modularization: Building semantic code graphs supports identifying critical entities, project structure visualization, and module partitioning (Borowski et al., 2023).
• Neural Code Generation: Conditioning sequence models on statically computed semantic facts improves generation of syntactically and semantically coherent code, especially for long-range dependencies and variable usage (Mukherjee et al., 2021).
• Feature-Aware Code Search: Semantic queries over abstract-interpreted attribute domains (e.g., “find predicates that, when called with a list and a var, return an int”) enable precise code discovery resilient to signature or naming differences (Garcia-Contreras et al., 2016).
• Binary Code Similarity and Reverse Engineering: Schema-enforced, LLM-extracted feature sets enable interpretable, indexable, and accurate cross-setting code similarity, with performance matching or exceeding black-box embedding baselines (Gagnon et al., 27 Sep 2025).
• Customizable Static Analyses: Compositional policy and neuro-symbolic integration allow users to define and orchestrate custom analyses with minimal additional tool development (Wang et al., 18 Dec 2024).

Empirical results consistently show notable improvements in precision, recall, and interpretability for systems leveraging explicit semantic feature extraction and representation.

5. Tooling Considerations and Best Practices

Enabling robust, scalable static semantic feature extraction demands modular, extensible tooling with the following characteristics:

• Modular Analysis Stages: Unpacking and parsing, token/feature extraction, optional data-flow analysis, category/profile matching, and anomaly or malware detection (Qadir et al., 2020).
• Updatable Feature Profiles: Regularly refine category signatures and behavioral mappings as software and threat landscapes evolve (Qadir et al., 2020).
• Hybrid Rule + Machine Learning Approaches: Combine expert rules for coarse filtering with machine-learned classifiers or neural encoders for nuance (Guan et al., 14 Feb 2024, Zhang et al., 2023).
• Scalability: Cache intermediate representations, parallelize feature extraction, and leverage efficient indexing (e.g., inverted index for human-interpretable features) (Gagnon et al., 27 Sep 2025, Borowski et al., 2023).
• Validation and Feedback Loops: Iteratively reconcile static predictions with empirical or manual labels; calibrate detection thresholds according to observed false-positive/negative rates (Qadir et al., 2020).
• Evasion and Obfuscation Mitigation: Detect and resolve reflection, dynamic code loading, and obfuscated identifiers using canonicalization, string resolution, or lightweight emulation (Qadir et al., 2020, Marais et al., 13 Jun 2025).
• Extensibility by Language/Domain: Adapt pipelines to new languages (e.g., via alternate parsers or keyword sets) and enable user-defined feature queries (e.g., via FQL (Zheng et al., 2019)).

A strong recommendation is to combine statically emitted semantic features with both rule-based and learned models, enabling robust and interpretable reasoning across diverse codebases.

6. Limitations, Challenges, and Research Directions

Although static semantic feature extraction has demonstrated effectiveness across security, comprehension, and generative modeling, notable challenges persist:

• Limitations of Syntactic Matching: Shallow keyword-based features (e.g., FQL patterns) lack execution-context awareness, resulting in limited semantic coverage (Zheng et al., 2019).
• Obfuscation and Dynamic Behaviors: Static analysis can be circumvented by code obfuscation, dynamic code loading, or reflection; hybrid or dynamic analysis may be required for full coverage (Qadir et al., 2020).
• Noise in Feature Extraction: Unfiltered diagnostic reports or low-signal static warnings can degrade performance; filtering and embedding selection strongly impact results (Guan et al., 14 Feb 2024).
• Semantic Drift and Context Loss: Path-based or CFG semantic features may still miss long-distance relationships or fail under complex control/data structures (Zhang et al., 2023).
• LLM Hallucination and Reliability: Language-model-based static analysis must carefully control prompt design, decomposition, and result validation to avoid semantic errors (Wang et al., 18 Dec 2024).
• Scalability in Neural Inference: LLM-based semantic feature extraction introduces computational costs (e.g., 0.15 s per icall in SEA) and prompt-token limits necessitating decomposition (Cheng et al., 8 Aug 2024).

Emerging research increasingly addresses these challenges with improved hybrid symbolic/neural pipelines, more expressive policy languages, indexable and interpretable feature schemas, and rigorous benchmarking. Integration with feedback-driven refinement and cross-domain analysis is expected to further close the gap between static abstraction and true behavioral semantics.

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References:

• "Automatic Feature Extraction, Categorization and Detection of Malicious Code in Android Applications" (Qadir et al., 2020)
• "Semantic-Enhanced Indirect Call Analysis with LLMs" (Cheng et al., 8 Aug 2024)
• "Automated Static Warning Identification via Path-based Semantic Representation" (Zhang et al., 2023)
• "Neural Program Generation Modulo Static Analysis" (Mukherjee et al., 2021)
• "scg-cli -- a Tool Supporting Software Comprehension via Extraction and Analysis of Semantic Code Graph" (Borowski et al., 2023)
• "Semantic Preprocessing for LLM-based Malware Analysis" (Marais et al., 13 Jun 2025)
• "FQL: An Extensible Feature Query Language and Toolkit on Searching Software Characteristics for HPC Applications" (Zheng et al., 2019)
• "Beyond Embeddings: Interpretable Feature Extraction for Binary Code Similarity" (Gagnon et al., 27 Sep 2025)
• "Enhancing Source Code Representations for Deep Learning with Static Analysis" (Guan et al., 14 Feb 2024)
• "Semantic Code Browsing" (Garcia-Contreras et al., 2016)
• "LLMSA: A Compositional Neuro-Symbolic Approach to Compilation-free and Customizable Static Analysis" (Wang et al., 18 Dec 2024)