GraphFuzz: Automated Testing of Graph Algorithm Implementations with Differential Fuzzing and Lightweight Feedback (2502.15160v1)

Abstract: Graph algorithms, such as shortest path finding, play a crucial role in enabling essential applications and services like infrastructure planning and navigation, making their correctness important. However, thoroughly testing graph algorithm implementations poses several challenges, including their vast input space (i.e., arbitrary graphs). Moreover, through our preliminary study, we find that just a few automatically generated graphs (less than 10) could be enough to cover the code of many graph algorithm implementations, rendering the code coverage-guided fuzzing approach -- one of the state-of-the-art search algorithms -- less efficient than expected. To tackle these challenges, we introduce GraphFuzz, the first automated feedback-guided fuzzing framework for graph algorithm implementations. Our key innovation lies in identifying lightweight and algorithm-specific feedback signals to combine with or completely replace the code coverage feedback to enhance the diversity of the test corpus, thereby speeding up the bug-finding process. This novel idea also allows GraphFuzz to effectively work in both black-box (i.e., no code coverage instrumentation/collection is required) and grey-box setups. GraphFuzz applies differential testing to detect both crash-triggering bugs and logic bugs. Our evaluation demonstrates the effectiveness of GraphFuzz. The tool has successfully discovered 12 previously unknown bugs, including 6 logic bugs, in 9 graph algorithm implementations in two popular graph libraries, NetworkX and iGraph. All of them have been confirmed and and 11 bugs have been rectified by the libraries' maintainers.

Evaluation of GraphFuzz: An Automated Fuzzing Framework for Graph Algorithms

The paper "GraphFuzz: Automated Testing of Graph Algorithm Implementations with Differential Fuzzing and Lightweight Feedback" presents a thorough investigation into the automated testing of graph algorithms, offering a novel methodology to address the inherent challenges posed by such tasks. The authors, Wenqi Yan, Manuel Rigger, Anthony Wirth, and Van-Thuan Pham, focus on the systematic testing of graph algorithm implementations and propose GraphFuzz as an innovative solution.

The central contribution of this work is the introduction of GraphFuzz, a feedback-guided fuzzing framework that moves beyond conventional code coverage strategies. The framework employs differential testing alongside lightweight and algorithm-specific feedback signals to detect logic errors within graph algorithm implementations. This approach is particularly effective for testing multi-language libraries where traditional coverage-collecting methods might falter.

Key numerical results underscoring the significance of GraphFuzz include the successful identification of 12 previously unknown bugs within widely-used graph libraries, NETWORKX and IGRAPH, of which 6 were logic bugs. The efficiency of the proposed framework is demonstrated by the remarkable improvement in the speed of bug detection — up to several thousand times faster in some cases compared to previous methods.

GraphFuzz operates in both black-box and grey-box setups, which is crucial for testing without reliance on source code availability. The core methodology leverages differential testing to compare outputs from different implementations, exposing discrepancies indicative of underlying logic bugs. The effectiveness of this approach is evident in its ability to handle complex graph constructs that are challenging to replicate manually.

Through extensive experimentation, the authors demonstrate alternatives to code coverage, using algorithm-specific outputs as feedback signals to guide the fuzzing process effectively. This is further supplemented by employing efficient mutation strategies specific to graph structures, leading to improved diversity in the test corpus and subsequently enhancing bug detection capability.

The implications of this research are considerable. In practical terms, the tools and methodologies developed here enable more efficient testing and maintenance of graph libraries, which play pivotal roles in crucial applications ranging from infrastructure design to real-time navigation systems. Theoretically, the work promotes a reevaluation of feedback signals in fuzzing, suggesting that lightweight, problem-specific signals can outperform traditional methods in certain domains.

Future developments in AI and software testing can build on the insights provided by GraphFuzz. There is potential for broader application across other algorithmic domains, as well as further refinement in feedback signal design and fuzzing strategies. Additionally, exploring the integration of metamorphic testing could further mitigate the limitations tied to the availability of multiple algorithm implementations.

In conclusion, GraphFuzz embodies an advanced testing framework that significantly advances the field of automated testing for graph algorithms. Its methodologies not only improve the testing efficiency but also expand the practical applicability of fuzzing techniques beyond their traditional confines, making substantial contributions to both academia and industry.