Software & AI
A Neurosymbolic Approach to Loop Invariant Generation via Weakest Precondition Reasoning
NeuroInv, a novel neurosymbolic framework, significantly advances automated program verification by reliably generating loop invariants. Integrating LLMs with Hoare logic and counterexample-guided symbolic repair, it achieves a 99.5% success rate on Java benchmarks, outperforming existing methods. Its dual-level refinement and repair mechanism enables robust invariant discovery, even for complex multi-loop programs, addressing a critical bottleneck in formal software verification.
Executive Summary: NeuroInv's Breakthrough in Loop Invariant Generation
99.5%
Success Rate (%)
+14.8%
Performance Boost (NeuroInv vs. NeuroInv*)
62%
Hard Benchmark Success Rate (%)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Dive into the core methodologies and empirical validations of NeuroInv, detailed through specific modules below.
Enterprise Process Flow
Program Segmentation
→
Backward-Chaining WP Reasoning
→
Candidate Invariant Generation
→
Hoare Logic Refinement
→
Symbolic Verification (OpenJML)
→
Counterexample-Guided Repair
| Method | Avg. Success Rate (%) | Key Strengths |
|---|---|---|
| NeuroInv | 99.5 |
|
| NeuroInv* | 84.7 |
|
| Ad-hoc Prompting | 66.4 |
|
| SpecGen | 28.5 |
|
99.5%
NeuroInv's Success Rate on Standard Benchmark
Scaling to Complex Programs: Hard Benchmark Results
NeuroInv was tested on a 'hard benchmark' of 10 larger multi-loop programs (averaging 7 loops each) to assess scalability. While performance naturally saw a dip compared to the standard benchmark, NeuroInv still achieved a significant 62% success rate, drastically outperforming ad-hoc prompting's 14%. This demonstrates its ability to handle more complex verification scenarios where neural reasoning chains are longer and more intricate, proving the value of its systematic approach over ad-hoc methods.
- Average loops per program: 7
- NeuroInv success rate: 62%
- Ad-hoc prompting success rate: 14%
- Identified neural module refinement as primary failure point (94% of failures occurred during neural refinement limit reached)
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