A comprehensive empirical study on bug characteristics of deep learning frameworks

• Published in: Information and software technology Vol. 151; p. 107004
• Main Authors: Yang, Yilin, He, Tianxing, Xia, Zhilong, Feng, Yang
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.11.2022
• Subjects: Bug characteristics; Bug detection; Deep learning framework; Empirical study; Deep learning framework; Bug detection; Bug characteristics; Empirical study
• ISSN: 0950-5849; 1873-6025
• DOI: 10.1016/j.infsof.2022.107004

Deep Learning (DL) frameworks enable developers to build DNN models without learning the underlying algorithms and models. While some of these DL-based software systems have been deployed in safety-critical areas, such as self-driving cars and medical diagnostics, for DL frameworks, characterizing their bugs and thus helping researchers to design specific quality assurance techniques become desperately needed. Our research aims to characterize bugs typical of DL frameworks at the source code level for an in-depth analysis of bug symptoms, root causes, and bug fixes. In this way, we hope to provide insights for researchers to design automatic quality assurance techniques, such as automatic repair techniques and fault location techniques, applicable to DL frameworks and DL-based software systems. We started by summarizing the DL framework reference architecture and proposing the DL framework bug taxonomy. Then, we mined 1,127 DL framework bug reports from eight popular DL frameworks and labeled the bug types, root causes, and symptoms. Finally, we discussed the bug characteristics and explored how developers could possibly deal with these bugs. Our main findings are: (i) DNN model building bugs and general type bugs accounted for one-third of the total defects. (ii) DNN model building bugs are more prone to algorithm logic constraints, internal API errors, and data/numerical errors. (iii) Fifteen bug-fixing patterns are summarized, providing reference for common DL framework bug repair and future research on the development of automatic DL framework bug detection tools. By analyzing the bug-fixing changes, we characterize the occurrences, root causes, symptoms, and fixing of these bugs. The study results have provided researchers with insights into how to ensure DL framework quality and presented actionable suggestions for DL framework developers to improve their code quality.