An Empirical Study on TensorFlow Program Bugs
Deep learning applications become increasingly popular in important domains such as self-driving systems and facial identity systems. Defective deep learning applications may lead to catastrophic consequences. Although recent research efforts were made on testing and debugging deep learning applications, the characteristics of deep learning defects have never been studied. To fill this gap, we studied deep learning application bugs collected from StackOverflow QA pages and Github projects. We extracted information from QA pages, commit messages, pull request messages, and issue discussions to examine the root causes and symptoms of these bugs. We also studied the strategies deployed by developers for bug detection and localization. These findings help researchers and developers to gain a better understanding of deep learning defects and point out new direction for future research.
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16:00 - 17:30 | Machine LearningISSTA Technical Papers at Zurich II Chair(s): Alex OrsoGeorgia Institute of Technology | ||
16:00 20mTalk | Compiler Fuzzing through Deep Learning ISSTA Technical Papers Chris CumminsUniversity of Edinburgh, Pavlos PetoumenosUniversity of Edinburgh, Alastair MurrayCodeplay Software, Hugh LeatherUniversity of Edinburgh | ||
16:20 20mTalk | Deep Specification Mining ISSTA Technical Papers Tien-Duy B. LeSchool of Information Systems, Singapore Management University, David LoSingapore Management University | ||
16:40 20mTalk | Identifying Implementation Bugs in Machine Learning based Image Classifiers using Metamorphic Testing ISSTA Technical Papers Anurag DwarakanathAccenture Labs, Manish AhujaAccenture Labs, Samarth SikandAccenture Labs, Raghotham M RaoAccenture Labs, R.P. Jagadeesh Chandra BoseAccenture Labs, Neville DubashAccenture Labs, Sanjay Podder | ||
17:00 20mTalk | An Empirical Study on TensorFlow Program Bugs ISSTA Technical Papers Yuhao ZhangPeking University, Yifan ChenPeking University, Shing-Chi CheungDepartment of Computer Science and Engineering, The Hong Kong University of Science and Technology, Yingfei XiongPeking University, Lu ZhangPeking University Pre-print | ||
17:20 10m | Q&A in groups ISSTA Technical Papers |