The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
A comprehensive study on deep learning bug characteristics
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
TensorFlow: A system for large-scale machine learning
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.08695 · Cited by 11,822
Caffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077
(2005) · PubMed · Cited by 7,011
Static Analysis of Shape in TensorFlow Programs
The Theano Development Team, Al‐Rfou, R., Alain, G., Almahairi, A., Angermueller, C., Bahdanau, D., Ballas, N., Bastien, F., Bayer, J., Belikov, A., et al. (2016) · arXiv (Cornell University) · doi:10.4230/lipics.ecoop.2020.15 · Cited by 1,589
Learning from mistakes: a comprehensive study on real world concurrency bug characteristics
Lu, S., Park, S., Seo, E., & Zhou, Y. (2008) · Proceedings of the 13th international conference on Architectural support for programming languages and operating systems · doi:10.1145/1346281.1346323 · Cited by 895
Orthogonal defect classification-a concept for in-process measurements
Chillarege, R., Bhandari, I. S., Chaar, J. K., Halliday, M. J., Moebus, D. S., Ray, B. K., & Wong, M.-Y. (1992) · IEEE Transactions on Software Engineering · doi:10.1109/32.177364 · Cited by 792
What are developers talking about? An analysis of topics and trends in Stack Overflow
Barua, A., Thomas, S. W., & Hassan, A. E. (2014) · Empirical Software Engineering · doi:10.1007/s10664-012-9231-y · Cited by 612
Torch: a modular machine learning software library
Collobert, R., Samy Bengio, A., & Mariéthoz, J. (2002) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 435
An empirical study on TensorFlow program bugs
Zhang, Y., Chen, Y., Cheung, S.-C., Xiong, Y., & Zhang, L. (2018) · Proceedings of the 27th ACM SIGSOFT International Symposium on Software Testing and Analysis · doi:10.1145/3213846.3213866 · Cited by 304
Software System Testing and Quality Assurance
Beizer, B. (1984) · Medical Entomology and Zoology · Cited by 249
Cited by
Testing machine learning based systems: a systematic mapping
Riccio, V., Jahangirova, G., Stocco, A., Humbatova, N., Weiss, M., & Tonella, P. (2020) · Empirical Software Engineering · doi:10.1007/s10664-020-09881-0 · Cited by 251
Giray, G. (2021) · Journal of Systems and Software · doi:10.1016/j.jss.2021.111031 · Cited by 187
Lost in Translation: A Study of Bugs Introduced by Large Language Models while Translating Code
Pan, R., Ibrahimzada, A. R., Krishna, R., Sankar, D., Wassi, L. P., Merler, M., Sobolev, B., Pavuluri, R., Sinha, S., & Jabbarvand, R. (2024) · Proceedings of the IEEE/ACM 46th International Conference on Software Engineering · doi:10.1145/3597503.3639226 · Cited by 133
Self-citationDocTer: documentation-guided fuzzing for testing deep learning API functions
Xie, D., Li, Y., Kim, M., Pham, H. V., Tan, L., Zhang, X., & Godfrey, M. W. (2022) · Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis · doi:10.1145/3533767.3534220 · Cited by 81
Muffin: testing deep learning libraries via neural architecture fuzzing
Gu, J., Luo, X., Zhou, Y., & Wang, X. (2022) · Proceedings of the 44th International Conference on Software Engineering · doi:10.1145/3510003.3510092 · Cited by 71
Metadata
- Authors
- Islam, M. J., Nguyen, G., Pan, R., & Rajan, H.
- Venue
- Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering
- Publisher
- ACM
- Published
- 2019-08-12
- Pages
- 510-520
- OpenAlex
- W2948096068
- Cited by
- 305
- References
- 20
- FWCI
- 26.94
- Type
- conference paper
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A comprehensive study on deep learning bug characteristics. https://app.cochat.ai/papers/10.1145/3338906.3338955
- The paper
- Islam, M. J., Nguyen, G., Pan, R., & Rajan, H. (2019). A comprehensive study on deep learning bug characteristics. Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. https://doi.org/10.1145/3338906.3338955
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
