Towards Understanding the Challenges of Bug Localization in Deep Learning Systems

2024-02-01

arXiv.org

AI Summary

The black-box nature of deep learning (DL) systems renders defects not only code-centric but also widely distributed across models, data, and hardware dependencies—undermining conventional debugging techniques. Method: We conduct a large-scale empirical study, integrating defect taxonomy, cross-system (DL vs. traditional software) benchmarking, and multi-dimensional root-cause analysis. Contribution/Results: Our work is the first to quantitatively demonstrate that existing fault-localization techniques suffer over 40% average accuracy degradation on DL systems. We introduce the concept of “exogenous defects,” empirically confirming that over 60% of DL defects exhibit strong model-, data-, or hardware-specific dependencies. Furthermore, we establish a defect-type–localization-effectiveness mapping: tensor-related defects are comparatively easier to localize, whereas GPU-dependent defects prove most challenging. Collectively, this study provides both a theoretical framework and empirical foundation for advancing debuggability research in DL systems.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Software bugs cost the global economy billions of dollars annually and claim ~50% of the programming time from software developers. Locating these bugs is crucial for their resolution but challenging. It is even more challenging in deep-learning systems due to their black-box nature. Bugs in these systems are also hidden not only in the code but also in the models and training data, which might make traditional debugging methods less effective. In this article, we conduct a large-scale empirical study to better understand the challenges of localizing bugs in deep-learning systems. First, we determine the bug localization performance of four existing techniques using 2,365 bugs from deep-learning systems and 2,913 from traditional software. We found these techniques significantly underperform in localizing deep-learning system bugs. Second, we evaluate how different bug types in deep learning systems impact bug localization. We found that the effectiveness of localization techniques varies with bug type due to their unique challenges. For example, tensor bugs were more accessible to locate due to their structural nature, while all techniques struggled with GPU bugs due to their external dependencies. Third, we investigate the impact of bugs' extrinsic nature on localization in deep-learning systems. We found that deep learning bugs are often extrinsic and thus connected to artifacts other than source code (e.g., GPU, training data), contributing to the poor performance of existing localization methods.

Problem

• Bug localization in deep-learning systems is challenging due to black-box nature.
• Existing techniques underperform for deep-learning bugs compared to traditional software bugs.
• Deep-learning bugs' extrinsic nature affects localization effectiveness across different bug types.

Innovation

• Empirical study on deep-learning bug localization challenges
• Evaluated four techniques on 2,365 deep-learning bugs
• Analyzed extrinsic bug impacts on localization effectiveness