CrossProbe: LLM-Empowered Cross-Project Bug Detection for Deep Learning Frameworks

Deep Learning (DL) models may introduce reliability challenges in the underlying DL frameworks. These frameworks may be prone to bugs that can lead to crash or wrong results, particularly when involving complex model architectures and substantial computational demands. Such framework bugs can disrupt DL applications, impacting customer experience and potentially causing financial losses. Traditional approaches to testing DL frameworks face limitations in adapting to the vast search space of model structures, diverse APIs, and the complexity of hybrid programming and hardware environments. Recent advancements using Large Language Models (LLMs) have improved DL framework fuzzing, but their efficacy depends heavily on the quality and diversity of input prompts, which are often constructed using single-framework data. In this paper, we propose an innovative approach for enhancing test generation for DL frameworks by leveraging “mirroring issues”—analogous bugs identified across different frameworks with common functionalities. Our approach is inspired by the fact that DL frameworks, such as PyTorch and TensorFlow, often share common bugs due to dependencies, developer errors, or edge-case inputs. We develop CrossProbe that utilizes LLMs to effectively learn from existing issues of one framework and transfer the acquired knowledge to generate test cases for finding mirroring issues in another framework, thus enabling cross-framework bug detection. To overcome the challenges of test case generation arising from the incompatible functionalities and different implementations between frameworks, we introduce three processes: alignment , screening , and distinction . These processes help mitigate transfer errors by establishing API pair databases, filtering unsuitable cases, and highlighting cross-framework distinctions. Experiments demonstrate that CrossProbe is efficient by saving 36.3% iterations of generation, and achieves a 25.0% higher success rate in issue transferring compared to existing state-of-the-art LLM-based testing techniques. CrossProbe detects 24 unique bugs using its transferred knowledge. Out of them, 19 are previously unknown and each requires cross-framework knowledge in deep learning for identification.