muRelBench: A Microbenchmarking Framework for Evaluating Operations on Zonotope Abstract Domains

Core Concepts

muRelBench is a framework for generating synthetic benchmarks to evaluate the performance of operations on Zonotope abstract domains, enabling comprehensive assessment of new algorithms and implementations.

Abstract

The paper presents muRelBench, a microbenchmarking framework for evaluating operations on Zonotope abstract domains, which are widely used in program and system verification. The framework allows for the dynamic generation of parameterized abstract states and the application of user-defined operations on them, along with the ability to check user-defined properties. The key features of muRelBench are: Dynamic generation of parameterized abstract states: The framework can generate Octagon abstract states with varying numbers of variables and connectivity between them. Application of user-defined operations: Users can provide implementations of operations, such as closure, to be tested on the generated abstract states. Checks for user-defined properties: The framework allows users to define checks to verify the correctness of the operations performed on the abstract states. The paper demonstrates the usefulness of muRelBench through a case study on evaluating different closure algorithms for the Octagon abstract domain. The results show that the framework can provide detailed insights into the performance characteristics of the algorithms, which may not be easily observable in the context of a full-fledged program analysis tool. The authors also discuss the extensibility of muRelBench beyond the Octagon domain, allowing users to add support for other abstract domains and operations. They invite contributions from the research community to further improve and expand the framework.

Stats

The average runtime for the Floyd-Warshall closure algorithm on the Fibonacci program was 144 ms with a standard deviation of 32.2 ms. The average runtime for the Chawdhary closure algorithm on the Fibonacci program was 117 ms with a standard deviation of 5.1 ms. The average runtime for the Floyd-Warshall closure algorithm on the Loop program was 46.8 ms with a standard deviation of 3.1 ms. The average runtime for the Chawdhary closure algorithm on the Loop program was 49.6 ms with a standard deviation of 10.3 ms.

Quotes

"Clearly, these two algorithms should have a different runtime growth with the increased number of variables." "For the plot with largest N = 100 we can see that, depending on the density value, one algorithm performs better than the other. For lower density states, Floyd-Warshall tends to perform better while for higher densities Chawdhary's incremental closure has better runtime data."

Key Insights Distilled From

muRelBench: MicroBenchmarks for Zonotope Domains

by Kenny Ballou... at arxiv.org 04-26-2024

https://arxiv.org/pdf/2404.16243.pdf

Deeper Inquiries

How can the muRelBench framework be extended to support other types of abstract domains beyond Octagons, such as Polyhedra or Intervals?

To extend the muRelBench framework to support other types of abstract domains like Polyhedra or Intervals, users would need to follow a similar approach to the existing implementation for Octagons. They would have to provide three additional classes: Abstract Container Type: Users would need to define a new abstract container type for the domain, such as PolyhedraDBM or IntervalDBM. Builder for the New Abstract Type: A builder class specific to the new abstract type would need to be implemented to handle the construction and manipulation of the abstract domain. State Type with Parameterization Sets: Users would have to create a state type that provides different parameterization sets for the JMH profiling tool, similar to how OctagonDBM is used in the current implementation. Additionally, users would need to extend operations over the new abstract domains by providing implementations for the desired operations. This would involve overriding various operations in the new abstract domain class with the specific implementations users wish to test. Furthermore, users would need to create additional benchmark classes, following the naming convention used in muRelBench, such as PolyhedraBench or IntervalBench, to include the new operations for evaluation.

What are the potential limitations or challenges in using synthetic benchmarks generated by muRelBench to accurately represent real-world program analysis scenarios?

While synthetic benchmarks generated by muRelBench provide a controlled environment for evaluating abstract domain operations, there are potential limitations and challenges in using them to accurately represent real-world program analysis scenarios: Limited Realism: Synthetic benchmarks may not capture the complexity and variability of real-world programs, leading to results that may not generalize well to practical applications. Overfitting: Benchmarks generated by muRelBench may inadvertently overfit to specific characteristics of the synthetic data, potentially biasing the evaluation results. Domain-Specific Challenges: Certain abstract domains or operations may exhibit behaviors in real-world scenarios that are not fully captured by synthetic benchmarks, limiting the generalizability of the evaluation. Scalability Concerns: Synthetic benchmarks may not scale well to larger or more complex programs, limiting their applicability to real-world program analysis scenarios. Performance Variability: The performance of abstract domain operations in synthetic benchmarks may not accurately reflect the performance in real-world scenarios due to differences in data distribution, program structures, and other factors.

How can the muRelBench framework be integrated with existing program analysis tools or frameworks to provide a more comprehensive evaluation of new abstract domain operations and algorithms?

To integrate the muRelBench framework with existing program analysis tools or frameworks for a more comprehensive evaluation of new abstract domain operations and algorithms, the following steps can be taken: API Integration: Develop an API that allows seamless communication between muRelBench and existing program analysis tools, enabling the exchange of data and results. Plugin Architecture: Implement a plugin architecture that allows users to easily incorporate muRelBench into their existing tools, providing flexibility and customization options. Data Exchange Formats: Support common data exchange formats to facilitate interoperability with different program analysis tools, ensuring compatibility and ease of integration. Workflow Automation: Enable automated workflows that streamline the process of running benchmarks, analyzing results, and integrating them back into the program analysis framework. Feedback Mechanism: Establish a feedback mechanism to gather insights from users on how muRelBench can better align with their specific program analysis needs, ensuring continuous improvement and relevance in the integration process.