Extreme Inlining in Compiler Optimization

Updated 23 December 2025

Extreme inlining is a compiler optimization technique that substitutes a vast majority of function call sites with their bodies, redefining traditional inlining limits.
It leverages aggressive cost model parameterization and ML-guided frameworks to improve performance through enhanced intra-procedural optimizations, although at the cost of increased binary size.
This approach significantly impacts static analysis, binary similarity, and formal verification by radically transforming code structure and call graph mappings.

Extreme inlining is a program transformation and compiler optimization strategy in which an unusually large fraction of function call sites are replaced—often recursively and across module boundaries—with the corresponding function bodies. This pushes the inlining ratio dramatically beyond conventional compiler defaults, yielding both opportunities and challenges for program performance, static analysis, ML-based binary analysis, and formal verification. Extreme inlining leverages aggressive parameterization of inliner cost models, exploitative control of compiler/linker flags, and, in the state of the art, integration with ML-guided inlining decision frameworks. The phenomenon fundamentally alters code structure, induces nontrivial impacts on downstream toolchains, and demands robust theoretical and empirical methods for correctness, efficiency, and security.

1. Definitions, Inlining Ratios, and Compiler Models

Semantics and Thresholds

In standard optimization regimes, function inlining replaces a call site with the body of its callee if (cost < threshold), where the cost and threshold are computed via an inliner’s cost model. In LLVM, parameters such as –inline-threshold, –inline-call-penalty, and function attributes (always_inline, noinline) collectively govern this process. Under –O3 or –Os, mean inlining ratios typically range from about 20% to 33% of functions, with maxima up to 66% for certain codebases (Abusabha et al., 16 Dec 2025, Jia et al., 2021).

Extreme inlining is characterized by systematic configuration—raising the inliner threshold orders of magnitude above defaults, optionally combining with full link-time optimization (LTO)—that produces inlining ratios up to 79.6% in practical settings (Abusabha et al., 16 Dec 2025). This level of inlining remains bounded by the compiler’s syntactic and semantic safety requirements, such as never-inlining rules for recursive or variadic functions, and is shaped by specific cost model parameters:

Parameter	Default	Extreme inlining settings
–inline-threshold	225–250	Up to 200,000+
–inline-call-penalty	25	0–5
Link-Time Optimization (LTO)	None/Thin	ThinLTO or Full LTO
Front-end flags	Usually off	–finline-functions, —force-inline

Extreme inlining at this scale transforms not just single call sites but entire transitive call graphs, generating binary functions (BFIs) that may embed dozens or hundreds of source-level callees (Jia et al., 2021, Abusabha et al., 16 Dec 2025).

2. Performance, Static Analysis, and Machine Learning Implications

Impact on Performance and Optimization Opportunities

Extreme inlining directly reduces call/return overhead and maximizes the scope for intra-procedural optimizations such as loop unrolling, vectorization, and register promotion. ML-guided inlining frameworks, notably MLGOPerf, demonstrate that by integrating fast speedup prediction models (such as IR2Perf) with reinforcement learning policies trained end-to-end, it is possible to aggressively inline and expose up to 26% more code regions to autotuning, yielding up to 3.7% speedup on established benchmarks compared to O3 (Ashouri et al., 2022). However, this comes with significant binary code size growth (12–24% larger), so optimal inlining policies must balance these tradeoffs adaptively.

Static Feature Shift and Binary Analysis

Aggressive inlining perturbs structural program features critical for static analysis and ML-based binary analysis:

Arithmetic instruction counts increase up to 200%
Call-graph edges and number of call sites can grow by 200–250%
Loop counts decrease (70% drop), while average loop size increases by up to 60%
Number of basic blocks can increase by 50%
Control flow graphs are deeply rewritten

These changes substantially degrade the performance of ML models for tasks such as binary code similarity detection, function name prediction, malware detection, and vulnerability search (Abusabha et al., 16 Dec 2025). Model F1 scores can fall by 12–40 percentage points, with recall for inlined cases suffering most. State-of-the-art DL-based classifiers (CNN, DNN) can see F1 drops from ~0.99 to ~0.78 for malware detection and ~0.87 to ~0.44 for malware family prediction under extreme inlining (Abusabha et al., 16 Dec 2025).

3. Binary Similarity, Matching, and Source Attribution

1-to-n and n-to-n Function Mappings

Traditional binary-to-source and binary-to-binary similarity frameworks assume a 1-to-1 mapping between binary and source functions. Extreme inlining violates this assumption, producing 1-to-n mappings: one BFI containing the bodies of multiple (n ≥ 2) source functions. n-to-n mapping scenarios, where different BFIs contain overlapping sets of source bodies, also arise but are less common (Jia et al., 2021, Jia et al., 2022).

Matching accuracy for binary similarity plummets under extreme inlining: | Task | No Inlining Recall@1 | Extreme Inlining Recall@1 | Drop | |----------------------|---------------------|---------------------------|------| | Code Search (CodeCMR)| ~80% | ~50% | –30% | | OSS-Reuse Detection | 58–71% (OSFs) | 6–10% (ISFs) | –~60%| | Vulnerability Detect | ~93% (NBF→NBF) | ~55% (BFI→BFI via ISFs) | –38% |

Simulation strategies (e.g., ASM2Vec one-layer inlining, Bingo recursive inlining) recover only ~60% of inlined source functions (Jia et al., 2021). Incremental, similarity-guided inlining can achieve up to 93% coverage but at greater computational expense.

Source Function Sets and ML Approaches

O2NMatcher introduces source function set (SFS) construction as a systematic “inlining-aware” alternative to 1-to-1 matching (Jia et al., 2022). By predicting inlined call-sites in the call graph using an ensemble of multi-label classifier chains (ECOCCJ48), O2NMatcher infers subtrees in the call graph likely to be inlined. It then aggregates the source bodies into SFSs, enabling downstream binary-to-source matchers to compare against both singletons and SFSs, boosting recall on inlined functions by over 6 percentage points versus state-of-the-art methods.

4. Formal Correctness, Verification, and Static Analysis

Correctness Criteria for Inlining

For higher-order languages, semantics-preserving inlining, particularly in the presence of free variables, demands both unique-target and environment-equivalence conditions (Bergstrom et al., 2013). Specifically, for a function $f$ with free variables $FV(f)$ , inlining at a call-site $c$ is permissible only if:

Unique-target: $\text{CFA}(c) = \{f\}$ (0CFA yields a singleton target set)
Environment-equivalence: $\forall x \in FV(f), E_{cap}(x) = E_{call}(x)$
In control-flow graph terms: no path exists from $f$ 's closure-capture to $c$ that rebinding any $x \in FV(f)$

Efficient 0CFA-based algorithms combined with graph reachability efficiently enforce these conditions at scale (Bergstrom et al., 2013).

Verification-Preserving Inlining

In the context of separation logic and formal program verification, extreme inlining challenges the preservation of verification guarantees. Verification-preserving inlining requires bounded safety and output monotonicity for "call-free" fragments, and bounded framing for inlined call/loop bodies (Dardinier et al., 2022). Soundness is mechanized in Isabelle/HOL: if a source program $(s, M)$ verifies under some annotation, so does its n-level inlined form, under a semantic condition $SC_M^n(T, s)$ . Implementation in Viper shows that efficient syntactic scans and light-weight structural checks are sufficient in practice to ensure correctness for the majority of real-world codebases.

Hybrid and Context-Sensitive Inlining in Static Analysis

Full top-down inlining in static analysis yields high precision but is computationally prohibitive due to "statement explosion." Hybrid inlining frameworks recognize that only a small fraction (<3%) of statements are "critical"—e.g., virtual calls, array indexing, non-linear arithmetic—that require context-sensitive propagation (Liu et al., 2022). Hybrid inlining builds mixed summaries where (i) non-critical statements are summarized under context-invariant abstractions, and (ii) critical statements are deferred and lazily inlined only in those call contexts where context sensitivity is required. Empirical results show that with <2.6% statements critical and average call-chain propagation <6, hybrid inlining achieves precision indistinguishable from infinite call-string top-down analyses, but at compositional-analysis cost.

5. Security Implications and Evasion Techniques

Extreme inlining enables the synthesis of evasive binary variants with unchanged source semantics but drastically modified static structure. ML-based malware detectors, vulnerability search engines, and function name predictors are susceptible to targeted evasion, as model behaviors are strongly dependent on call-graph, control-flow, and feature statistics, which are all radically altered by aggressive inlining (Abusabha et al., 16 Dec 2025). Adversaries can exploit compiler flag combinations (e.g., raising –inline-threshold to 200,000 and enabling full LTO) to engineer binaries that subvert previously robust detection boundaries.

Recommendations for robust ML-based binary analysis systems under extreme inlining regimes include:

Compiler-aware data augmentation: train on variants spanning mild to extreme inlining
Adversarial training with aggressive and randomized inliner configurations
Inlining-aware preprocessing (e.g., de-inlining heuristics) to recover higher-level code structure
Prefer semantic or dynamic features over raw syntactic counts
Mandate evaluation against variants compiled with “unknown” inliner flagologies to benchmark true robustness

6. Practical Recommendations and Limitations

Across analyses, practical guidelines for managing extreme inlining effects include:

Use aggressive, but targeted, inliner parameterizations only when downstream benefits (autotuning, vectorization, performance) outweigh code-size and analytic loss.
Hybrid and incremental inlining strategies, guided by empirical or ML-based similarity/precision metrics, help control inlining cost by focusing on critical paths or match-improving branches (Jia et al., 2021, Liu et al., 2022).
In symbolic or logic-based verification, apply syntactic or structural semantic checks prior to inlining; avoid inlining for features known to invalidate monotonicity or framing (Dardinier et al., 2022).
Whenever binary similarity, code search, or OSS reuse detection is a requirement, generate and use SFSs to account for 1-to-n mappings, and conditionally deploy inlining simulation only when empirical provenance indicates significant inlining.

However, limitations persist: 0CFA and bounded call/string sensitivity can block safe inlining when environments merge; partial or cross-module inlining introduces hard-to-model accuracy gaps; and current static aggregation over-approximates function body set semantics.

7. Future Directions

Open research directions include:

Integration of higher-order context sensitivity (beyond 0CFA) with efficient inliner decision procedures (Bergstrom et al., 2013)
Semantic AST merging for more precise SFS representation in binary-to-source matching (Jia et al., 2022)
Abstract reference counting (RC-CFA) to sharpen static analysis precision in large-scale codebases (Bergstrom et al., 2013)
Automated defenses for ML-based binary analysis utilizing de-inlining and dynamic feature extraction (Abusabha et al., 16 Dec 2025)
ML-guided end-to-end compiler optimization with context- and security-aware inliner policies (Ashouri et al., 2022, Abusabha et al., 16 Dec 2025)

Extreme inlining thus stands both as a lever of performance and a disruptor of existing program analyses, requiring innovation in both theoretical frameworks and pragmatic toolchains for robust, performant, and secure program compilation and analysis.