Bi-Directional Analysis Engine

• Bi-directional analysis engine is a unified platform that integrates scalable value-flow analysis with symbolic reasoning for precise program evaluation.
• It leverages a high-throughput Datalog fixpoint engine alongside SMT-backed symbolic execution to flexibly manage context-sensitive constraints.
• The architecture achieves significant performance gains by dynamically switching between algebraic and SMT modes, reducing overhead in static analyses.

A bi-directional analysis engine refers, in this context, to a unified platform for program analysis that seamlessly integrates both value-flow analysis (typified by scalable, data-flow-centric reasoning as in Datalog) and symbolic analysis (encompassing SMT-backed symbolic execution and Datalog-native algebraic reasoning) (Diamantakis et al., 1 Aug 2025). The primary aim of such an engine is to enable joint, efficient, and context-sensitive analyses—facilitating workflows that require precise value tracking, symbolic constraint solving, and their combination within a single system. Desyan exemplifies this paradigm, expanding modern production-ready Datalog engines (notably Souffl"e) with symbolic modules and external SMT solver integration, creating a highly flexible and performant infrastructure for hybrid program analysis.

1. Integration of Value-Flow and Symbolic Reasoning

Desyan’s engine is designed to bridge a longstanding separation in the static analysis landscape between value-flow analysis (e.g., data-flow/points-to analysis via Datalog) and symbolic reasoning techniques (such as symbolic execution with SMT solvers). The integration is realized by tightly coupling:

• A best-in-class Datalog fixpoint engine (providing high-throughput, scalable value-flow computations)
• Full-fledged SMT solving via industry-standard SMT engines for handling complex logical constraints
• An algebraic, Datalog-native symbolic reasoning layer that supports lightweight constraint evaluation bottom-up without resorting to external SMT calls unless necessary

These mechanisms co-exist within a single framework, enabling analyses to interleave symbolic constraint solving and data-flow propagation in a demand-driven and performance-aware manner.

2. Engine Architecture and Reasoning Strategies

The core architectural design allows for flexible selection between three modes of reasoning, as dictated by the needs of a particular analysis:

• Pure Value-Flow (Datalog-based): Employs the Souffl"e engine or similar for high-performance data-flow programming, excelling in analyses where constraints are expressed as join patterns or simple relationships.
• SMT-backed Symbolic Analysis: Invokes external SMT solvers for path-sensitive or concolic analysis tasks that require solving complex logical conditions.
• Datalog-Native Symbolic Reasoning: Enables algebraic, bottom-up evaluation of simple symbolic constraints for cases not warranting full-blown SMT invocation (e.g., lightweight conditionals within path-sensitive fixpoint computations).

The system’s abstraction over the reasoning backend is agnostic, facilitating switching or blending strategies at the level of the analysis construct.

3. Performance and Scalability

Desyan’s engine demonstrates significant performance advantages:

• For value-flow analysis, it leverages the Souffl"e Datalog evaluator, achieving execution times more than 20× faster than less optimized approaches.
• Lightweight symbolic reasoning (performed Datalog-natively) achieves over 2× speedups relative to strategies that indiscriminately delegate to SMT solvers; this is particularly impactful in analyses with frequent, simple conditional constraints.
• Where full SMT evaluation is unavoidable (e.g., in concolic engines or symbolic evaluators with rich path conditions), Desyan integrates external solvers with minimal overhead, delegating only when algebraic reasoning is insufficient.

The design thus optimally allocates computational resources, minimizing both call overhead and redundant work.

4. Application Patterns and Supported Analyses

Desyan’s paradigm covers a wide spectrum of analyses, including:

• Classical points-to and data-flow analysis (Datalog-centric, with fixpoint computation)
• Path-sensitive static analyses that require frequent constraint discharge (handled, if simple, via algebraic modules; otherwise, via SMT)
• Symbolic execution and concolic testing, where program paths with complex, compound constraints are analyzed integrating both value-flow and combinatorial SMT reasoning

Constructs are provided to automate commonly re-occurring analysis patterns, allowing analysts to write composite analyses that naturally blend both reasoning modes within the same codebase. This unification is significant for industrial-scale static analysis infrastructures.

5. Technical Innovations and Reasoning Modes

Several features underpin Desyan’s bi-directional analysis capacity:

• The integration with SMT solvers is agnostic, permitting the replacement or configuration of external decision procedures to suit the precision-performance spectrum.
• The platform’s Datalog-native algebraic reasoning is implemented in a bottom-up style, preserving the fixpoint semantics necessary for value-flow propagation but extended with symbolic state tracking.
• Automatic and efficient handling of transitions between symbolic and value-flow modules is provided. This enables analyses to opportunistically resolve constraints locally or escalate when expressive power is insufficient.
• For program constructs such as conditionals, path merging, and external calls, seamless switching between analysis modes is enabled.

6. Significance for Program Analysis Research

Desyan marks a notable advance in practical static analysis engine design by fusing value-flow and symbolic reasoning within a general, performant platform (Diamantakis et al., 1 Aug 2025). This design overcomes the prevalent challenge of composing multi-paradigm analyses and removes the need for analysts to trade scalability against expressiveness. Applications include security analysis, concolic execution for bug finding, precise data provenance, and beyond.

A related line in the field is the use of Binary Decision Diagrams (BDDs) as explored in the research of Ondřej {a}k (McGill, 2006), which investigates BDDs for representing Boolean functions in program analysis; this suggests ongoing interest in alternative data structures for scalable flow-sensitive reasoning.

7. Future Directions and Implications

Potential future research will involve:

• Further automating mode selection between algebraic and SMT-based reasoning to optimize both accuracy and performance;
• Exploring richer hybrid symbolic/data-flow analyses that integrate dynamic (runtime) traces with static reasoning;
• Adopting additional decision procedures (beyond SMT) for other semantic domains.

A plausible implication is that integrated bi-directional analysis engines such as Desyan could standardize multi-modal static analysis infrastructure in both academic and industry settings—making seamless, hybrid valuation-symbolic reasoning routine rather than exceptional.