STAB: Specification-driven Testing for Algorithmic Bottlenecks

STAB is a specification-driven testing pipeline that generates test cases exposing algorithmic bottlenecks by extracting constraints and injecting adversarial structures from natural language problem specifications. The method improves bottleneck detection rates from 50-57% to 71-73% across major programming languages and LLM implementations.

STAB addresses a critical gap in software quality assurance: generating test cases that expose algorithmic worst-case scenarios. Traditional approaches rely on brute-force input scaling or implementation-specific adversarial inputs, which fail to capture the structural conditions that trigger algorithmic bottlenecks. This research matters because algorithmic efficiency directly impacts system performance, resource costs, and user experience—particularly relevant in resource-constrained environments like blockchain systems and embedded AI applications.

The problem stems from the complexity of understanding algorithmic behavior. Developers often miss edge cases where inputs satisfy all constraints but force pathological execution paths. STAB's two-phase approach—constraint saturation and adversarial structure injection—separates concerns intelligently. The constraint saturator uses optimization techniques to assign realistic maximum values within problem bounds, while the adversarial injector leverages a curated catalog of implementation-level attack patterns. By grounding generation in natural language specifications, STAB enables systematic, reproducible testing without implementation knowledge.

The performance gains are substantial: 23-percentage-point improvements in bottleneck detection across language-LLM combinations suggest STAB captures patterns traditional methods miss. Consistency across Python, Java, and C++ indicates the approach generalizes well. For developers and organizations, this translates to higher-confidence performance testing and reduced risk of deploying inefficient code. In domains like cryptocurrency where computational efficiency affects transaction throughput and validator economics, algorithmic optimization carries direct business value.

Future work likely involves expanding the adversarial scenario catalog, improving constraint resolution for complex specifications, and integrating with continuous integration pipelines. The open-source release enables community feedback and practical validation.

• →STAB generates algorithmic bottleneck test cases from natural language specifications without requiring implementation details.
• →The pipeline combines constraint optimization with adversarial pattern injection, achieving 71-73% bottleneck detection versus 50-57% baseline rates.
• →Performance improvements hold consistently across Python, Java, C++, and both open and closed-source LLMs.
• →The method addresses critical gaps in software quality assurance by exposing worst-case algorithmic behavior systematically.
• →Open-source availability enables integration into developer workflows and further research in automated testing.