A comprehensive study on deep learning bug characteristics

By Md Johirul Islam, Giang Nguyen, Rangeet Pan, Hridesh Rajan. ESEC/SIGSOFT FSE, 2019

361 citations recorded by the source corpus.

Deep learning has gained substantial popularity in recent years. Developers mainly rely on libraries and tools to add deep learning capabilities to their software. What kinds of bugs are frequently found in such software? What are the root causes of such bugs? What impacts do such bugs have? Which stages of deep learning pipeline are more bug prone? Are there any antipatterns? Understanding such characteristics of bugs in deep learning software has the potential to foster the development of better deep learning platforms, debugging mechanisms, development practices, and encourage the development of analysis and verification frameworks. Therefore, we study 2716 high-quality posts from Stack Overflow and 500 bug fix commits from Github about five popular deep learning libraries Caffe, Keras, Tensorflow, Theano, and Torch to understand the types of bugs, root causes of bugs, impacts of bugs, bug-prone stage of deep learning pipeline as well as whether there are some common antipatterns found in this buggy software. The key findings of our study include: data bug and logic bug are the most severe bug types in deep learning software appearing more than 48% of the times, major root causes of these bugs are Incorrect Model Parameter (IPS) and Structural Inefficiency (SI) showing up more than 43% of the times.We have also found that the bugs in the usage of deep learning libraries have some common antipatterns.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/17750e76d4d3a33b1eada72f39238acd0246bd37

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Citations in the corpus

Taxonomy of Real Faults in Deep Learning Systems (2019, background): “The final version of the Another work worth mentioning is a DL bug characterisation by Islam et al. [15].”
Software Engineering for AI-Based Systems: A Survey (2021, background): “Three of these papers conducted empirical studies examining bugs in ML projects [89, 188, 225], whereas one paper proposed a specialized debugger for ML models [35].”